Generating and using a predictive virtual personification

ABSTRACT

A system for generating a predictive virtual personification includes a wearable data capture device, a data store, and a saliency recognition engine, wherein the wearable data acquisition device is configured to transmit one or more physio-emotional or neuro-cognitive data sets and a graphical representation of a donor subject to the saliency recognition engine, and the saliency recognition engine is configured to receive the one or more physio-emotional or neuro-cognitive data sets, the graphical representation, and one or more identified trigger stimulus events, locate a set of saliency regions of interest (SROI) within the graphical representation of the donor subject, generate a set of SROI specific saliency maps and store, in the data store, a set of correlated SROI specific saliency maps generated by correlating each SROI specific saliency map a corresponding trigger event.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of andpriority to U.S. application Ser. No. 14/804,302 filed on Jul. 20, 2015,which claims the benefit of and priority to U.S. Provisional PatentApplication Ser. No. 62/026,568 filed on Jul. 18, 2014, U.S. ProvisionalPatent Application Ser. No. 62/148,709 filed on Apr. 16, 2015, U.S.Provisional Patent Application Ser. No. 62/148,719 filed on Apr. 16,2015, and U.S. Provisional Patent Application Ser. No. 62/148,724 filedon Apr. 16, 2015, the contents of each of which are incorporate hereinby reference.

TECHNICAL FIELD

The disclosed technology relates generally to applications for cognitiveanalysis, and more particularly, some embodiments relate to systems andmethods for predictive virtual personification using cognitive andgeospatial signal analysis.

BACKGROUND

With improvements in imaging and computer processing power, computergenerated representations of human subjects have become more common,particularly in the film and video game industries. While realisticcomputer generated representations/characters have become increasinglymore realistic, currently available technologies still require real-timeinteraction or scenario-specific planning to enable the character tointeract within a particular virtual environment. Accordingly, acomputer generation of a real-life person is only as realistic to theextent that a programmer pre-determined the character's response to anyparticular stimulus.

The psychological and anatomical movement characteristics of thereal-life subject must be manually and painstakingly programmed into thecharacter's computer program. Alternatively, the real-life subject maywear special suits and, using motion capture technology (MOCAP), acomputer can capture the anatomical movements and responses to specificstimuli, but again, the interaction of the virtual character with thevirtual environment is manually manufactured. If the real-life subjectdid not perform a specific task or reaction, then the character is notcapable of performing the task or reaction either.

Moreover, existing computer generated character technology is notcapable of incorporating cognitive behavior from the real-life subjectinto the character. Cognitive behavior, for purposes of this disclosure,means the level of learning and/or awareness a subject may have to anyspecific stimulus. As cognitive learning increases, a subject's reactionto the same stimulus will become more repeatable and more predictable.Alternatively, when cognitive learning is low, a subject's response to aparticular stimulus is more sporadic. This concept is true for largescale reaction to stimuli, such as reacting to a baseball being pitchedin a subject's direction, as well as small scale reactions such asfacial expressions and anatomical movement characteristics. Whiletechnology exists to functionally image the human brain and determinewhen certain neural pathways are active in response to specific stimuli,available technology has been incapable of incorporating functionalimaging techniques to create a more cognitively aware computer generatedrepresentation of a subject. Thus, currently available virtualpersonification technology is incapable of adequately incorporating asubject's cognitive capabilities with realistic anatomical features andmovements.

BRIEF SUMMARY OF EMBODIMENTS

According to various embodiments of the disclosed technology, a methodfor generating a predictive virtual personification using cognitive andgeospatial signal analysis. A predictive virtual personification may be,for example, a graphically rendered, holographic, robotic, mechanicaldoppelganger, or other representative form that both looks and behaveslike a donor subject (e.g. a human) when exposed to the same stimulus.For example, data may be collected from observations made of the donorsubject performing particular tasks and reacting to particular stimuli.The data may be processed by a predictive rendering engine to output apredictive virtual personification that, when exposed to either the samestimuli as the donor subject or a completely new stimuli, reacts in away that realistically emulates the donor.

In one embodiment, a method for generating a predictive virtualpersonification includes capturing a static geospatial imaging baseline,capturing a static neuro-cognitive imaging baseline, correlating thebaseline image data with historical data, and simultaneously capturingdynamic geospatial and neuro-cognitive imaging while a subject performsan activity. For example, static geospatial imaging modalities mayinclude optical imaging, magnetic resonance imaging (MRI), computertomography imaging (CT), X-Ray, or other geospatial imaging techniquesknown in the art. “Static” imaging means that the subject being imagedis stationary, whereas “dynamic” imaging means the subject is moving.Static neuro-cognitive imaging modalities may include Functional MRI(fMRI), functional Positron Emission Tomography (PET),Magnetoencephalography (MEG), Electroencephalography (EEG), or otherfunctional brain imaging as known in the art. Dynamic geospatial imagingmodalities may include optical imaging, and dynamic neuro-cognitiveimaging modalities may include MEG and EEG.

Some embodiments may also include calculating and storing stimulusspecific cognitive plasticity factors and graphically rendering avirtual personification from geospatial data. For example, cognitiveplasticity factors may quantitatively depict the level of cognitivelearning that has occurred with respect to a subject's reaction to aspecific stimulus. For purposes of defining a cognitive plasticityfactor, a particular subject's physical reaction to a particularstimulus is presumed to be a manifestation of the activation of aparticular set of neurons in that subject's brain, known as a neuralpathway. The aforementioned neuro-cognitive imaging may detect theneural pathway activation, while the geospatial imaging may detect thephysical manifestation of the neural pathway activation. A subject'sfirst few reactions to repeated exposures to the same stimulus likelywill result in the activation of varying neural pathways as correlatedwith slightly different physical reactions—measured as a high cognitiveplasticity factor. However, over time, a single neural pathway willactivate repeatedly to the same stimulus as correlated with the samesubject-unique physical reaction—measured as a low cognitive plasticityfactor. Being a reaction to a particular stimulus. Thus, cognitivelearning (and repeatability of a particular physical reaction to thesame known stimulus) will increase as an inverse relationship to thecognitive plasticity factor as defined herein.

In some examples, graphical rendering of a virtual personification fromgeospatial data is accomplished using available data compiled from threedimensional geospatial imaging and then extrapolated using knownhumanoid standards or canons to predictively render specific physicalreactions to stimuli. In some embodiments, the cognitive plasticityfactor may be incorporated in a predictive graphical rendering algorithmto determine, probabilistically, a specific subject's reaction to aparticular stimuli based on the subject's cognitive plasticity withrespect to that specific stimuli. Thus, an example method for generatinga predictive virtual personification may also include selecting astimulus and applying the stimulus specific cognitive plasticity factorto the predictive graphical rendering algorithm.

In other embodiments, a method for training a neural pathway may includecapturing a static geospatial imaging baseline, capturing a staticneuro-cognitive imaging baseline, correlating the baseline imaging tohistorical data, and simultaneously capturing dynamic geospatial andneuro-cognitive imaging while a subject performs an activity. An examplemethod may also include repeating the static neuro-cognitive imagingwhile the subject visualizes performing the same activity to measure thesubject's cognitive plasticity factor—the subject's cognitive plasticityfactor will reduce inversely to the level of cognitive learning that hasoccurred, until a threshold level is reached indicating that the subjecthas learned the particular task sufficiently.

Some examples include a system for generating a predictive virtualpersonification using cognitive and geospatial signal analysis. Thesystem may include one or more static geospatial imaging devices, one ormore static neuro-cognitive imaging devices, one or more dynamicgeospatial imaging devices, one or more dynamic cognitive imagingdevices, and a correlation engine, wherein all of the imaging devicesare configured to transmit image data to the correlation engine, and thecorrelation engine is configured to calculate a stimulus specificcognitive plasticity factor. An example system may also include acorrelation database, historical data feed, and data store, wherein thecorrelation database may be configured to receive and correlatehistorical data from the historical data feed and imaging data from theimaging devices and store results in the data store. An example systemmay also include a predictive rendering engine and a 3D renderingengine, wherein the 3D rendering engine is configured to receivesimaging data and historical data and render 3D images of a subject, andthe predictive rendering engine is configured to receive 3D renderings,correlation data, and cognitive plasticity factors, calculate aprobabilistic 3D rendering of a subject responding to a specificstimulus.

Other features and aspects of the disclosed technology will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, which illustrate, by way of example, thefeatures in accordance with embodiments of the disclosed technology. Thesummary is not intended to limit the scope of any inventions describedherein, which are defined solely by the claims attached hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

The technology disclosed herein, in accordance with one or more variousembodiments, is described in detail with reference to the followingfigures. The drawings are provided for purposes of illustration only andmerely depict typical or example embodiments of the disclosedtechnology. These drawings are provided to facilitate the reader'sunderstanding of the disclosed technology and shall not be consideredlimiting of the breadth, scope, or applicability thereof. It should benoted that for clarity and ease of illustration these drawings are notnecessarily made to scale.

FIG. 1 illustrates an example system for generating a predictive virtualpersonification using cognitive and geospatial signal analysis that maybe used in implementing various features of embodiments of the disclosedtechnology.

FIG. 2 illustrates an example predictive rendering engine that may beused in implementing various features of embodiments of the disclosedtechnology.

FIG. 3 is a flow chart illustrating a method for training a neuralpathway consistent with embodiments of the disclosed technology.

FIG. 4 illustrates an example system for generating training a neuralpathway consistent with embodiments of the disclosed technology.

FIG. 5 is a flow chart illustrating a method for generating a predictivevirtual personification using cognitive and geospatial imaging analysis.

FIG. 6 is a diagram illustrating a system for generating a predictivevirtual personification, consistent with embodiments disclosed herein.

FIG. 7 is a diagram illustrating a method for generating a predictivevirtual personification, consistent with embodiments disclosed herein.

FIG. 8 is a flow chart illustrating a method for identifying and storinga donor subject's physio-emotional characteristics, consistent withembodiments disclosed herein.

FIG. 9 is a diagram illustrating an example set of saliency regions ofinterest within a donor subject, consistent with embodiments disclosedherein.

FIG. 10A is a chart illustrating a relationship between movement in onedimension of a saliency region of interest as correlated with a stimulusevent and a corresponding action event, consistent with embodimentsdisclosed herein.

FIG. 10B is a chart illustrating a relationship between movement in onedimension of a saliency region of interest as correlated with a stimulusevent and a corresponding action event, consistent with embodimentsdisclosed herein.

FIG. 10C is a chart illustrating a relationship between movement in onedimension of a saliency region of interest as correlated with a stimulusevent and a corresponding action event, consistent with embodimentsdisclosed herein.

FIG. 11 illustrates an example an audio-visual (AV) data capture device,consistent with embodiments disclosed herein.

FIG. 12 illustrates a method for generating and using a predictivevirtual personification, consistent with embodiments disclosed herein.

FIG. 13 illustrates an example computing module that may be used inimplementing various features of embodiments of the disclosedtechnology.

The figures are not intended to be exhaustive or to limit the inventionto the precise form disclosed. It should be understood that theinvention can be practiced with modification and alteration, and thatthe disclosed technology be limited only by the claims and theequivalents thereof.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The technology disclosed herein is directed toward a system and methodfor generating a predictive virtual personification using geospatial andneuro-cognitive signal analysis. Some embodiments of the disclosureprovide a method for training a neural pathway. An example method oftraining a neural pathway may include capturing, with a geospatialimaging modality, one or more geospatial imaging data sets, capturing,with a neuro-cognitive imaging modality, one or more neuro-cognitiveimaging data sets, and applying a stimulus. The method may furtherinclude calculating, with a computer processor, a stimulus specificcognitive plasticity factor (CPF) and re-applying the stimulus until theCPF exceeds a threshold level. If the CPF exceeds the pre-determinedthreshold, then a the subject has effectively learned a response to thestimulus. This learning mechanism provides the subject, and thesubject's trainers, with real-time feedback indicating how well thesubject is learning a desired stimulus response.

In some embodiments, a method for generating a predictive virtualpersonification (PVP) includes receiving, from an AV data source, an AVdata set and locating within the AV data set, with a saliencyrecognition engine, a graphical representation of a donor subject and aset of saliency regions of interest (SROI) within the graphicalrepresentation of the donor subject. For example, an SROI may be aparticular observable feature on a donor subject, such as an eye brow, amouth, an arm, a hand, etc., and the method includes observing thatfeature over time and tracking movements in correlation to a triggerstimulus event, as well as the movements of other SROIs of the samedonor subject at the same times.

The method may also include identifying one or more trigger stimulusevents, wherein each trigger stimulus events precedes or iscontemporaneous with one or more SROI specific reactive responses andeach SROI specific reactive response is observable within a SROI. Forexample, the trigger stimulus event may be an oncoming baseball pitch, achange in lighting, a question posed by another subject/person, or aninternal decision of the donor subject to do something. The SROIspecific reactive responses are movement maps (e.g., SROI-specificsaliency maps) that track changes in geospatial orientation over timefor each SROI.

The method may also include generating, for each SROI, a set of SROIspecific saliency maps, wherein each SROI specific saliency map plots achange in geospatial orientation of one or more SROIs within apredetermined time-frame corresponding to each trigger stimulus event,and storing, in a data store, a set of correlated SROI specific saliencymaps generated by correlating each SROI specific saliency map acorresponding trigger event.

Some examples of the method include identifying a set of donor-specificphysio-emotional characteristics corresponding to a donor-specificphysio-emotional state at the time of the trigger stimulus event andtagging the set of correlated SROI specific saliency maps with thecorresponding set of donor-specific physio-emotional characteristics.For example, the donor-specific physio-emotional characteristics mayinclude a donor subject's mood (e.g., happy, sad, angry, etc.), as wellas other factors such as stress level, level of rest, health,performance, etc. In some examples, the donor-specific physio-emotionalcharacteristics may be manually entered by a user into a user inputdevice, such as a computer terminal, mobile phone, laptop, or other userinput device with a user interface sufficient to enable data input. Inother examples, the set of donor-specific physio-emotionalcharacteristics may be automatically matched to the SROI specificsaliency maps (i.e., the captured data set) using a predictive virtualpersonification (PVP) correlation engine programmed onto a PVP server.For example, one or more correlated SROI specific saliency maps may bematched/correlated with a plurality of historical SROI specific saliencymaps, wherein each historical SROI specific saliency map corresponds toa set of known physio-emotional characteristics. In some examples, thematching of the one or more correlated SROI specific saliency maps witha plurality of historical SROI specific saliency maps may be performedby applying a renormalization group transformation to each historicalspecific saliency map to generate a predictive saliency map space.

In some embodiments, the method may also include generating, with agraphical rendering engine, an animated representation of the donorsubject using the AV data set. The method may further include exposingthe animated representation of the donor subject to a secondary stimulusevent and rendering for each SROI, with a PVP rendering engine, apredicted reactive response. For example, a previously applied triggerstimulus event, or completely new stimulus event may be introduced(e.g., throwing the representation of the donor subject a football, whenprevious trigger stimulus events included throwing a baseball and abasketball), and a predicted active response, as applied to each SROI,may be calculated. For example, the method may include identifying asecondary set of physio-emotional characteristics corresponding to theanimated representation of the donor subject (e.g., predicting oridentifying the representation of the donor subject's mood, stresslevel, rest level, health, etc.), and identifying one or more triggerstimulus events corresponding to the secondary stimulus event (i.e.,throwing football is similar to throwing a baseball or basketball). Themethod may further include receiving, from the data store, each set ofcorrelated SROI specific saliency maps corresponding to each identifiedtrigger stimulus event and to the identified set of physio-emotionalcharacteristics and generating, with the PVP rendering engine, a set ofpredictive SROI-specific saliency maps based on a probabilisticextrapolation as a function of the correlated SROI specific saliencymaps, the identified physio-emotional characteristics, and theidentified trigger stimulus event.

Some examples of the method include generating a set of predictiveSROI-specific saliency maps by collecting the correlated SROI specificsaliency maps into a historical saliency map space and applying arenormalization group transformation to the historical saliency mapspace to generate a predictive saliency map space.

The method may further include rendering, with the graphical renderingengine, a geospatial movement of the animated representation of thedonor subject by applying the set of predictive SROI-specific saliencymaps to each SROI within the animated representation of the donorsubject.

In other embodiments of the disclosure, a system for generating apredictive virtual personification includes an AV source, a data store,and a saliency recognition engine. For example, the AV source may be ahistorical archive comprising subsequently captured film, video, oraudio data, a video camera, a television camera, a high frame rate videocamera, a high resolution video camera, a motion capture device (MOCAP),a functional imaging modality, or other AV data sources as known in theart. The AV data source may be configured to transmit one or more AVdata sets to the saliency recognition engine, wherein each AV data setincludes a representation of a donor subject. The saliency recognitionengine may include a non-volatile computer readable media with acomputer program stored thereon, the computer program configured toreceive the AV data set and one or more identified trigger stimulusevents, wherein each identified trigger stimulus events precedes or iscontemporaneous with one or more SROI specific reactive responses andeach SROI specific reactive response is observable within an SROI. Thesaliency recognition engine may be further configured to locate a set ofsaliency regions of interest (SROI) within the graphical representationof the donor subject and generate, for each SROI, a set of SROI specificsaliency maps. For example, each SROI specific saliency map may plot achange in geospatial orientation of one or more SROIs within apredetermined time-frame corresponding to each trigger stimulus event.The saliency recognition engine may be further configured to store, inthe data store, a set of correlated SROI specific saliency mapsgenerated by correlating each SROI specific saliency map a correspondingtrigger event.

In some examples, saliency recognition engine is further configured toidentify a set of donor-specific physio-emotional characteristicscorresponding to a donor-specific physio-emotional state at the time ofthe trigger stimulus event and tag the set of correlated SROI specificsaliency maps with the corresponding set of donor-specificphysio-emotional characteristics. The system may also include agraphical rendering engine configured to generate an animatedrepresentation of the donor subject based on the AV data set. The systemmay also include a PVP rendering engine configured to generate apredicted reactive response to a secondary stimulus event.

In some examples, the PVP rendering engine is further configured toidentify a secondary set of physio-emotional characteristicscorresponding to the animated representation of the donor subject andidentify one or more trigger stimulus events corresponding to thesecondary stimulus event, receive each set of correlated SROI specificsaliency maps that correspond to each identified trigger stimulus eventand to the identified set of physio-emotional characteristics andgenerate a set of predictive SROI-specific saliency maps based on aprobabilistic extrapolation as a function of the correlated SROIspecific saliency maps, the identified physio-emotional characteristics,and the identified trigger stimulus event.

In some embodiments, the graphical rendering engine configured to rendergeospatial movement of the animated representation of the donor subjectby applying the set of predictive SROI-specific saliency maps to eachSROI within the animated representation of the donor subject. Someexamples of the system include an AV output device (e.g., a video ormovie projector, a display, a holographic projector, etc.) configured toproject the animated representation of the donor subject into ageospatial environment. The geospatial environment may be rendered orreal.

Other embodiments of the disclosure provide a system for training aneural pathway. The system may include an environmental isolationdevice, one or more optical imaging modalities, and one or moreneuro-cognitive imaging modalities. Each optical imaging modality mayoptically couple to the environmental isolation device and may beconfigured to capture an optical image data set of a subject locatedwithin the environmental isolation device and transmit optical imagingdata set to the correlation engine. Each neuro-cognitive imagingmodality may be configured to capture a functional imaging data set of abrain, identify, within the functional imaging data set, any activeneural pathways, and transmit the functional imaging data set to thecorrelation engine. The correlation engine may be configured tocorrelate the optical imaging data set with the functional imaging dataset and to calculate a stimulus specific CPF.

Other embodiments of the disclosure provide a method for generating apredictive virtual personification. The method may include capturing,with a geospatial imaging modality, one or more geospatial imaging datasets, capturing, with a neuro-cognitive imaging modality, one or moreneuro-cognitive imaging data sets, and applying a stimulus. The methodmay also include calculating, with a computer processor, a stimulusspecific CPF, graphically rendering, with a rendering engine, a threedimensional virtual personification, and correlating, with a correlationengine, the stimulus specific CPF with the three dimensional virtualpersonification.

FIG. 1 illustrates an example system for generating a predictive virtualpersonification using cognitive and geospatial signal analysis that maybe used in implementing various features of embodiments of the disclosedtechnology. Referring now to FIG. 1, a system for generating apredictive virtual personification may include one or more staticgeospatial imaging modalities 110. For example, static geospatialimaging modality 110 is configured to capture images of stationarysubject. Static geospatial imaging modality 110 may be either aninternal imaging modality to capture internal anatomy, such as an X-Ray,CT Scanner, MRI, Ultrasound, or other imaging modality designed tocapture images of a subject's internal anatomy. Alternatively, or incombination with an internal imaging device, static geospatial imagingdevice 110 may be an external imaging device to capture images of asubject's external anatomy such as optical imaging cameras or laserscanners, or other imaging modalities designed to capture images of asubject's external anatomy.

Still referring to FIG. 1, the system for generating a predictivevirtual personification may also include one or more staticneuro-cognitive imaging modalities 120. For example, staticneuro-cognitive imaging modality 120 may be either a neuro-functionalimaging modality, a cognitive imaging modality, or a combination ofboth. A neuro-functional imaging modality is designed to detectactivation of neurons and/or neural pathways in the brain through thedetection of electro-magnetic fields that are generated when neuronsactivate (e.g. by using an EEG or MEG device), or through the detectionof increased blood flow to specific regions of the brain surroundingactivated neurons that tend to draw in more oxygen (e.g., detection of aBlood Oxygen Level Dependent signal or BOLD with fMRI), or throughdiffusion tensor imaging (DTI).

A cognitive imaging modality may incorporate one or more of theneuro-functional imaging modalities, but uses historical correlation totrack variance in neural pathway activation as correlated with theperformance (or a subject's imagining of performance) of a specifictask. The cognitive imaging modality may then calculate a cognitiveplasticity factor (CPF) associated with the particular task, or stimulus180, that represents the level of predictability of a subject's neuralresponse to that task or stimulus. For example, a low CPF indicates ahigh level of neural pathway variance in response to repeated exposuresto the same stimulus (e.g. if a subject with a low CPF to a startlingevent would react differently to repeated exposure to the same startlingevent, and different neural pathways would activate upon each exposure).Alternatively, a high CPF indicates a low level of neural pathwayvariance in response to repeated exposures to the same stimulus, meaningthat upon each exposure to the same stimulus, the same neural pathwaywill activate, as manifested by the same physical response to thestimulus.

Still referring to FIG. 1, the system for generating a predictivevirtual personification may also include one or more dynamic geospatialimaging modalities 150. For example, dynamic geospatial imagingmodalities 150 may include optical imaging devices such as digital videocameras. An example digital video camera might be a high-frame-ratecamera (e.g. some high frame rate cameras are capable of capturingupwards of 18,000 frames per second and are capable of time mapping,warping, high dynamic rate to low dynamic rate conversion, and/or tonemapping). In some examples, geospatial imaging modalities 110 and 150may interact with a video processing module 135 configured to execute avideo extrapolation and rendering algorithm that may predictivelycalculate and interleave missing video pixel data in order to render acomplete virtual image of a particular subject. For example, the highframe rate camera may capture partial imaging data depicting a subjectperforming a specific activity, and the video processing module 135 mayextrapolate the partial data to complete a fully rendered image of thesubject.

Alternatively, the video processing module 135 may extrapolate missinginterleaved frames to convert low frame rate video data to high framerate video data. For example, both static geospatial imaging modality110 and dynamic geospatial imaging modality 150 may be a mobile phone ortablet camera, or any other consumer camera device capable of capturingstill and video images and uploading the images via wirelesscommunications standards, or via the Internet, to video processingmodule 135 for processing.

In other embodiments, dynamic geospatial imaging device 150 may includea motion capture device (MOCAP). For example, MOCAP technology mayinvolve placing one or more acoustic, inertial, light emitting diode(LED), magnetic, or reflective markers on one or more attachment pointson a subject, and then using a detector paired with the particularmarker, in combination with digital video, to precisely capture thethree-dimensional location of each marker and attachment point. The datamay then be processed using video processing module 135 to generate athree dimensional rendering of the subject that dynamically changes overtime in correlation to the subject's actual movements. Other forms ofgeospatial image capture may be used as are known in the art.

Still referring to FIG. 1, the system for generating a predictivevirtual personification may also include one or more dynamicneuro-cognitive imaging modalities 140. Example dynamic neuro-cognitiveimaging modalities 140 may include MEG or EEG devices. For example, anMEG device is configured to be worn on a subject's head and detectchanges in magnetic fields emanating from a particular region of thesubject's brain when electric signals travel down specific neuralpathways. The signals may then be processed by video processing module135 to calculate a three dimensional map of neural pathway activation.Similarly, an EEG device is also configured to be worn on a user's headand detect changes in electrical fields emanating from a particularregion of the subject's brain. Either EEG or MEG results may showspecific areas of interest containing neural pathway activation. Inother examples, other direct and/or indirect neural-cognitive imagingdevices may be used as would be known in the art. For example,eye-tracking devices that capture images of a subject's eyes as thatsubject performs specific activities to determine the level of focus onthe activity, and repeatable patterns of eye movements, may be used todetermine the subject's CPF with respect to that particular activity. Inother words, a subject with a low CPF may exhibit more random eyemovements when repeatedly performing the same activity (e.g. aninexperienced basketball free throw shooter may exhibit very random eyemovement patterns when shooting free throws). In contrast, a subjectwith a high CPF may exhibit more repeatable and deliberate eye movementswhen repeatedly performing the same activity (e.g. an experiencedbasketball free throw shooter may exhibit the same exact pattern of eyemovements, with deliberate focus on a target, when shooting freethrows). Other embodiments may incorporate sensors and actuators, suchas accelerometers, to collect additional data to correlate and storewith the imaging data sets. For example, data from an accelerometerembedded in a mobile device may be incorporated with image data from themobile device camera. The combined data set may be correlated and usedin rendering a virtual dynamic personification (e.g. the additionalsensor data may be used to supplement image data in rendering smooth,lifelike geospatial movement).

In some embodiments, the system for generating a predictive virtualpersonification may calculate a time-dependent CPF, or tCPF. Similar tothe CPF, a tCPF is proportional to and/or a measure of the degree towhich a subject learns a particular response to a particular stimulus.Whereas a standard CPF measures the degree of predictability in thesubject's neuro-cognitive response (i.e. the degree of repeatability inneural pathway selection in response to the repeated exposure to thesame stimulus), the tCPF is a normalized measure of the response speedto a particular stimulus. For example, a subject responding to aparticular stimulus for the first time (e.g. swinging a bat in anattempt to hit an oncoming baseball) may take longer to process theresponse then a subject who has performed that same task multiple times.The improvement in response speed, Δt, can be measured using theneuro-cognitive imaging modalities by not only determining which neuralpathway activates in response to the stimulus, but also determining howlong it takes for the neural pathway to completely activate. Throughrepeated exposure to the same stimulus, not only may the predictabilityimprove with respect to which neural pathway activates, but the responsetime between stimulus and neural pathway activation may decrease. Thesystem for generating a predictive virtual personification may calculatethe tCPF by measuring changes in neural pathway response time Δt, asvisualized using neuro-cognitive imaging modalities, over repeatedexposures to the same stimulus. The system may, using a computerprocessor, normalize these response time changes by taking Δt₀ as theresponse time to a first exposure to a stimulus, and Δt_(n) as theresponse time to a the nth (i.e. the most recent) exposure to the samestimulus, and taking tCPF as the ratio in Equation 1.

$\begin{matrix}{{tCPF} = \frac{\Delta \; t_{n}}{\Delta \; t_{0}}} & (1)\end{matrix}$

Still referring to FIG. 1, the system for generating a predictivevirtual personification may also include a correlation engine module130. Correlation engine module 130 may accept imaging data as input fromeach of the static geospatial imaging modalities, each of the staticneuro-cognitive imaging modalities 120, each of the dynamic geospatialimaging modalities 150, and each of the dynamic neuro-cognitive imagingmodules 140, as well as video processing module 135. For example,baseline image data from static geospatial imaging modalities 110,including internal and external imaging modalities, may be mergedtogether to form a three dimensional rendered anatomically accuratelikeness of a subject. That likeness may then correlated to threedimensional neuro-cognitive image and CPF and/or tCPF baseline data fromstatic neuro-cognitive imaging modalities 120.

In some examples, the static data may be further correlated with imagedata captured from dynamic geospatial imaging modalities 150 showingmovement of a subject performing a specific activity or reacting to aspecific stimulus (e.g. swinging a baseball bat at an oncoming pitch orswinging a golf club), and image and/or CPF/tCPF data from dynamicneuro-cognitive imaging modalities 140. The resulting data sets,including trends of CPF and/or tCPF data as correlated to specificmovements of a subject's anatomy in response to specific stimuli, may bestored in a correlative database 170, and stored in data store 174. Theresulting data sets may include a set of three-dimensionally renderedgeospatial representations of a subject correlated over time withexposure to a stimulus and CPF and/or tCPF data. For example, the datasets stored in correlative database 170 may include a three-dimensionalrepresentation of a golfer swinging a golf club to strike a ball,including specifically the precise geospatial positioning of thegolfer's hands and arms as he completes the golf swing. That data maythen be correlated and stored together with CPF and/or tCPF data showingwhat neural pathway(s) are activated each time the golfer swings theclub at the ball and/or the neural pathway activation response time. Therepeatability of anatomical positioning during the swing may then becorrelated with repeatable neural pathway activation and/or loweredneural pathway activation response time, resulting in a high CPF and/orlow tCPF.

In some embodiments, correlation engine module 130 may generate acorrelation matrix similar to that shown in Equation 2 (for time t=0)and Equation 3 (for time t=n). Equation 2 illustrates a baselinecorrelation function P(t=0), for a give stimulus exposure or activity attime t=0 (i.e. the image data is captured by the imaging modalities 110and 120 to calculate a baseline state), where each geospatial coordinatex₁ to x_(n) as correlated with each stimulus specific CPF and/or tCPF,S₁. to S_(n).

$\begin{matrix}{{P\left( {t = 0} \right)} = \begin{bmatrix}{S_{1} \cdot x_{1,{t = 0}}} & \ldots & {S_{1} \cdot x_{n,{t = 0}}} \\\vdots & \ddots & \vdots \\{S_{n} \cdot x_{1,{t = 0}}} & \ldots & {S_{n} \cdot x_{n,{t = 0}}}\end{bmatrix}} & (2)\end{matrix}$

In some embodiments, because the static nature of this baselinecorrelation, a particular activity may be imagined (instead ofphysically performed). For example, the golfer may imagine swinging agolf club and striking a ball while static geospatial andneuro-cognitive image sets are captured. Repeating the process enables acomparison of which neural pathways are activated, and a calculation ofthe CPF and/or tCPF as a function of the neural pathway variance.

Equation 3 illustrates a dynamic correlation function P(t=n), for agiven stimulus exposure or activity at time t=n (i.e. the image data iscaptured by the dynamic imaging modalities 140 and 150 to calculate adynamic state), where each geospatial coordinate x₁ to x_(n) ascorrelated with each stimulus specific CPF and/or tCPF S₁. to S_(n). forall times from t=1 to t=n. In other words, each three dimensional voxelfor each rendered image frame may be correlated with a stimulus specificCPF and/or tCPF illustrating the cognitive plasticity acquired by thesubject for movement of that specific voxel for a given stimulus.

$\begin{matrix}{{P\left( {t = n} \right)} = \begin{bmatrix}{S_{1} \cdot x_{1,{t = n}}} & \ldots & {S_{1} \cdot x_{n,{t = n}}} \\\vdots & \ddots & \vdots \\{S_{n} \cdot x_{1,{t = n}}} & \ldots & {S_{n} \cdot x_{n,{t = n}}}\end{bmatrix}} & (3)\end{matrix}$

In several embodiments, the static and dynamic correlations may berepeated and stored in correlative database 170 and data store 174.Moreover, some examples include receiving historical data fromhistorical data feed 172. The historical data may be video or stillimages, either external (e.g. video or still pictures from a digitaloptical camera) or internal in nature (e.g. X-Rays, MRIs, CT Scans,Ultrasounds). The historical data may give additional visual referencepoints for a subject as exposed to a specific stimulus, and may even beused to empirically calculate a CPF and/or tCPF by demonstrating therepeatability of specific geospatial responses by the subject to thesame stimulus and/or the physically observable response times. Thehistorical data may then be compiled in correlative database 170 andprocessed by correlation engine module 130 to supplement other image andCPF and/or tCPF data. Accordingly, in some embodiments, a predictivevirtual personification may be generated from the historical data setsalone by correlating empirically calculated CPF and/or tCPF data withhistorical image data, and rendering the virtual personificationaccordingly. In that example, static modalities 110 and 120 and dynamicimage modalities 140 and 150 are not required to generate a predictivevirtual personification.

In several of these embodiments, the predictive quality of a virtualpersonification may be extrapolated by predictive rendering engine 200,and the virtual personification may be extrapolated and rendered by 3Drendering engine 160. For example, a subject geospatial response to astimulus may be rendered in three dimensions by 3D rendering engine 160,and then correlated to a CPF and/or tCPF through correlation enginemodule 130 and stored in correlative database 170. Then, in a virtuallyrendered environment, the virtual personification may be exposed to aparticular stimulus or given a specific activity to perform, such thatpredictive rendering engine 200 may determine, probabilistically, how asubject may respond by accessing responses to similar stimuli in thecorrelative database 170 and extrapolating to a probabilistically likelyresponse based on the previous response(s) and a stimulus specific CPFand/or tCPF. If the stimulus specific CPF and/or tCPF is sufficientlyhigh, then the predictive rendering engine will determine that therendered response to the new stimulus is likely to be similar to thesubject's previously captured response. In contrast, if the CPF and/ortCPF is low, then the predictive rendering engine will determine thatthe rendered response to the new stimulus is likely to be more random.

FIG. 2 illustrates an example predictive rendering engine. An examplepredictive rendering engine 200 may include an context encoding module210 that is configured to convert and categorize input data intocomputer readable contextual data sets. For example, context encodingmodule 210 may include an audio context encoding sub-module 212 and ageospatial context encoding sub-module 214, wherein each contextencoding sub-module may be configured to receive input data 180 andconvert the input data into a predictive rendering context using one ormore context conversion algorithms. For example, the context conversionalgorithms may interpret and catalog the input data into predeterminedcategories with specific context related parameters (e.g. a baseballapproaching at a particular speed with particular geospatial coordinatesin relation to a baseball bat being held by the predictive virtualpersonification). The conversion algorithms may also append responsetags to the contextual data sets. The response tags, for example, maycorrespond to anticipated emotional states (e.g. surprise, shock,sadness, happiness, elation, fear, etc.).

In some examples, the input data 180 may be historical data, geo-spatialimaging data, or neuro-cognitive imaging data as described with respectto FIG. 1. Input data 180 may include environmental and/or interactivedata from a rendered or real environment. In some embodiments of thisdisclosure, the predictive virtual personification may be configured tointeract within a graphically rendered environment. The renderedenvironment could be a three dimensional rendering of a particular realor imaginary environment, such as would be depicted in a video game, amovie, a television broadcast, and Internet broadcast, or othermultimedia depiction. Moreover, other rendered characters may alsointeract with environment. Input data 180 may include a set of changesto the rendered environment (e.g. characters moving within theenvironment or changed environmental conditions).

In several embodiments of this disclosure, the response tags correlatemay observe emotional responses that particular donor subject may haveexhibited in response to the same or similar stimuli, as recorded bygeospatial and neuro-cognitive imaging. For example, a system forgenerating a predictive virtual personification, as disclosed withrespect to FIG. 1, may be used to detect a donor subject's emotionalstate as triggered by specific stimuli based on neuro-cognitiveresponses (e.g. detecting neural pathways that are activated when adonor subject is surprised, happy, sad, etc.), as correlated withspecific facial and body language responses (e.g. smiling, crying,wincing, flinching, etc.). These neuro-cognitive and geospatialresponsive data sets may be stored with respective correlated responsetags in data store 174. Context encoding module 210 may then assign oneor more response tags to input data 180 through known audio and videosignal analysis techniques to identify a specific stimulus (e.g. anapproaching baseball) by correlating the analyzed data with historicaldata catalogs to assign a known response tag to the input data. Theresponse tag may then be used as an index to retrieve a set of contextspecific rendered and historical response parameters, as well as a setof personality factors, from data store 174. For example, historicalresponse parameters may include specific image data sets of facialexpressions and body language associated with the respective responsetag for a particular donor subject, and rendered response parameters mayinclude context appropriate rendered graphical data to fill in gapswhere no historical data is available (e.g. no image data of thatparticular emotional response was ever captured for the particular donorsubject).

The historical response parameters and the rendered response parametersmay be compiled by rendering module 250 to generate one or more threedimensional graphical video image data sets depicting a possiblereaction to the input data set 180, visualized as a multidimensionalpossible graphical response vector

_(i) for each i=0 to m, and wherein m possible graphical responses aregenerated by graphics rendering engine 254 based on available historicaland rendered response parameters. The historical response parameters andthe rendered response parameters may also be compiled by renderingmodule 250 to generate one or more audio data sets depicting a possibleaudible reaction to the input data set 180, visualized as amultidimensional possible audio response vector,

_(j), for each j=0 to n, and wherein n possible audio responses aregenerated by audio rendering engine 252 based on available historicaland rendered response parameters. Together, possible response vectors

_(i) and

_(j) make up the possible audio visual response space AV(i,j) asillustrated by Equation 4.

AV(i,j)_(0,0) ^(m,n)={

_(i),

_(j)}, for 0≦i≦m; 0≦j≦n   (4)

Still referring to FIG. 2, a predictive rendering engine 200 may alsoinclude a predictive personality analyzer 220. Predictive personalityanalyzer 220 may receive context specific rendered and historicalresponse parameters, as well as donor subject specific personalityfactors w_(k) (for each k between 0 and r, and wherein r personalitydimensions stored in data store 174 for the particular donor subject)and calculate predictive personality-based reactions to a particularstimulus. For example, personality factors w_(k) may includeconscientiousness, agreeableness, extraversion, openness, neuroticism,or other known personality dimensions. This data can be compiled andstored in data store 174 based on personality tests taken by the donorsubject (e.g. Briggs-Meyers personality tests), from input by otherpersons who may know or have known the donor subject, or from historicaldata sets that include video and audio data demonstrating the donorsubject's reactions to various stimuli. Predictive personality analyzer220 may apply the personality factors, along with the response tag(s),to generate a personification probability factor PPF(w_(k)).Personification probability factor PPF(w_(k)) incorporates a donorsubject's observed tendencies to assign a probability to each responsevector

_(i) and

_(j) to modify the possible audio visual response space AV(i,j)_(0,0)^(m,n) into a probable audio visual response space PAV(i,j) asillustrated by Equation 5.

PAV(i,j)_(0,0) ^(m,n)={PPF(w _(ik))

_(i), PPF(w _(jk))

_(j)}, for 0≦i≦m; 0≦j≦n; 0≦k≦r   (5)

In several example embodiments, predictive personality analyzer 220 maycalculate and output a predictive probability factor PPF(w_(k)) for eachdonor subject and each input data set 180, and rendering module 250 maythen receive both audio visual response space AV(i,j) from data store174 and the personification probability factor PPF(w_(k)) from thepredictive personality analyzer 220, to generate the probably audiovisual response space PAV(i,j). Accordingly, speech rendering engine maythen calculate a probabilistic audible response vector,

, to input data set 180, and graphics rendering engine 254 may calculateand render a probabilistic three dimensional video response vector,

to input data set 180.

Still referring to FIG. 2, a predictive rendering engine 200 may alsoinclude expression rendering engine 256, incorporated in renderingmodule 250. Expression rendering engine 256 may receive historicalgraphical data from data store 174 and combine with personificationprobability factor PPF(w_(k)) to calculate a probabilistic expressionvector

. Both response vectors

and

may be modified by expression vector

and interlaced into a single audio visual output 280 by synchronizationengine 258.

In some examples, predictive personality analyzer 220 may also calculatea mood parameter, μ(l), based on general long term personality trendfactors l (i.e. moods), that have been observed in the donor subject orthat are available from historical data. For example, mood parameterμ(l) may be applied as a coefficient to modify personificationprobability factor PPF(w_(k)), resulting in a mood-dependentpersonification probability factor μ(l) PPF(w_(k)). Accordingly,rendering module 250 may output mood dependent audio visual responsevectors μ(l)

and μ(l)

.

In some embodiments of the disclosure, the predictive virtualpersonification may be configured to interact with a real environment(e.g. as a hologram, robot, mechanical doppelganger, or otherrepresentative form of the donor subject). Input data 180 may include aset of changes to the real environment as captured by geospatial imagecapture equipment, audio capture equipment, or other environmentalmonitoring equipment.

In some examples, a system for generating a predictive virtualpersonification includes analyzing and storing a rhythmic entrainmentcycle. As used herein, a rhythmic entrainment cycle describes a learnedpattern of responses an individual may exhibit when exposed to aparticular stimulus. For example, an individual may develop specifichabits or mannerisms, such as baseball players who always take the samestance in the batter's box, perform the same rituals before the pitchcomes, or swinging in a certain style in response to different types ofpitches. While these mannerisms and habits may have not existed when thebaseball player first started playing baseball, they did take hold overmany days, weeks, month, or years of performing the same tasks in thesame ways. All individuals may exhibit similar mannerisms and/or habits,that form overtime, in response to many different stimuli (e.g. sneezingor coughing a certain way, driving a car with one hand on the wheel,knocking on a door, etc.). These mannerisms and habits form specificneural pathways in the brain that correlate to the repeated reactions tostimuli, and as the neural pathways become more stable, responses becomequicker to the same stimuli, and more predictable. Thus, rhythmicentrainment is the process whereby these predictable responses to thesame repeated stimuli take form, and the level of rhythmic entrainmentcan be measured by analyzing either neuro-cognitive image data setswhile these specific tasks are being performed and measuring a CPFand/or tCPF, as described with respect to FIG. 1, or by measuringhistorical geospatial data sets acquired over time of a donor subjectreacting to the same stimuli, or by a combination of both methods.Accordingly, the CPF, or other measurements of rhythmic entrainment, maybe applied by predictive rendering engine 200 to modify personificationprobability factor PPF(w_(k)). For example, a higher level of rhythmicentrainment of a response to a particular stimuli will be directlyproportional to a higher stimuli-specific personification probabilityfactor PPF(w_(k)).

Still referring to FIG. 2, output data set 280 may be referred to as adonor's personified self DPS=PAV(i,j). The DPS, then, may be configuredto react to object within either a real or rendered environment (e.g.walls, doors, chairs, or other objects in a room, faces, expressions, ormovements from another person, etc.). In an in-phase mode, thepredictive virtual personification may interact with all changes to therendered environment, including changes caused by other renderedcharacters, whereas in an out-of-phase mode, the predictive virtualpersonification may only interact with changes to environmentalconditions, but not with other characters in the environment.

FIG. 3 is a flow chart illustrating an example method for training aneural pathway. Referring to FIG. 3, the method may include capturing astatic geospatial image baseline at step 310, capturing a staticneuro-cognitive baseline at step 320, calculating a CPF and/or tCPF atstep 330, and correlating and storing the geospatial, neuro-cognitive,and CPF and/or tCPF data at step 340. Some embodiments may also includecapturing dynamic geospatial and dynamic neuro-cognitive imaging while asubject performs an activity or reacts to a stimulus at step 350. Themethod may also include recalculating the CPF and/or tCPF at step 260and repeating steps 320, 330, 340, 350, and 360 of the method until theCPF and/or tCPF surpasses a predetermined threshold value (i.e. thesubject learns to perform the task or react to the stimulus sufficientlywell).

EXAMPLE 1

A novice golfer desires to perfect his putting capability. First, staticgeospatial image sets of the golfer are taken to capture the golfer'sanatomical features. These image sets may include optical imaging and CTscanning to capture the golfer's anatomy. A three dimensional renderingof the golfer may then be calculated. Then, baseline staticneuro-cognitive image sets of the golfer are taken. Static neuroimagingsets may be taken with MEG, EEG, or FMRI to get a baseline view of theneural pathway responses to specific activities, including putting agolf ball. To statically capture the golfer's neural pathway activation,the golfer may simply imagine he is putting the golf ball while fMRI,EEG, and/or MEG images are captured, illustrating which neural pathwayscause the golfer to think about putting the ball (which will betheoretically similar to the same pathways that actually activate if thegolfer really puts the ball, with some minor deviation such asactivation of the motor cortex). If the golfer repeats this processmultiple times of imagining putting the golf ball while neural imagingis performed, the neural pathway repeatability and/or variance can bemeasured showing how much variance there is in the specific neuralpathways responsible for putting a golf ball. From that measurement, aCPF can be calculated and stored with the other baseline data.Alternatively, the neural pathway activation response time can bemeasured to calculate a baseline Δt₀. The task can then be repeated anda Δt₁ can be calculated and compared to the Δt₀ to calculate a baselinetCPF₀.

After baseline images are captured and stored, the golfer may actuallydynamically perform the movements with his arms and hands required toput a golf ball while dynamic geospatial and neuro-cognitive image setsare captured. For example, the neuro-cognitive image sets may becaptured on MEG and/or EEG modalities while optical high-frame-ratevideo, standard video, and/or MOCAP technology is used to capture thegeospatial movement. The MEG and EEG data can be correlated with thebaseline neuro-cognitive data to demonstrate correlation in theactivated neural pathways, and the dynamic geospatial data can becorrelated with the static baseline geospatial data to provideadditional data points for three-dimensional rendering of a virtualpersonification of the golfer.

Still referring to Example 1, the golfer repeats the putter swingmultiple times, each time repeating the image capture. The geospatialdata can be compared from swing to swing to detect variances in theswing, and those variances can be correlated with neural pathwayvariance and/or response time, as applied in a CPF and/or tCPF value.Initially, a novice golfer should have a low CPF (and/or high tCPF) ascorrelated with a relatively high degree of swing variance. However, asthe golfer learns to put more precisely, the CPF value will increase(and/or tCPF value will decrease) along with the putting precision, andneural pathway variance decrease and geospatial movement variance willdecrease. These changes, as tracked by the increasing CPF (and/ordecreasing tCPF) as correlated with geospatial swing repeatability canbe tracked and displayed as feedback to the golfer until satisfied thatthe swing is learned.

The same method illustrated in Example 1 may be applied to any learnedactivity or stimulus response, such as swinging a bat, swinging a tennisracket, playing an instrument, driving a car, flying a plane, shooting aweapon, or any other training activity. Moreover, the activity andlearning paradigm may incorporate other stimuli, such as changed playingconditions, dangerous obstacles, weather, or other stimuli that thesubject may then learn to properly respond to the stimuli. For example,an airline may train its pilots to respond to wind shear or engine outthreats by using this same training paradigm, and may require that eachpilot achieve a specific threshold CPF and/or tCPF value before beingallowed to fly a real plane. Other example applications of this trainingmethod are possible as would be understood in the art.

EXAMPLE 2

As illustrated in FIG. 2, another example application of the learningparadigm entails rehabilitating injured patients. Certain brain or spineinjuries may cause a patient to lose the use of specific neuralpathways, and thus, certain activities may be impacted. For example, astroke patient may lose the ability to speak without slurring wordsbecause a specific neural pathway becomes damaged. However, the patientmay learn to use a new neural pathway to perform the same activity. Thesame method described in FIG. 2 is applicable. First, baselinegeospatial and neuro-cognitive images may be taken, correlated, andrendered to illustrate how the patient's mouth moves while speaking andwhat neural pathways are active. Without the injury, the same neuralpathway would be repeatability activated, but with the injury, thatpathway may be damaged.

Still referring to Example 2, as new neural pathways are sought by thepatient's brain, neural pathway variance may be high, and CPF low(and/or neural pathway activation response time may be high and tCPF maybe high). In this learning paradigm, historical data may be imported tocompare geospatial imaging of the subject performing the same tasks(e.g. speaking certain words) before the stroke. The geospatial data canbe compared to help correct motor and speech, and as those correctionsare made, CPF and/or tCPF values can show how well the subject islearning to use new neural pathways to accomplish the same tasks. Thesubject will be successful when the CPF and/or tCPF value surpasses apredetermined threshold level. The same learning paradigm may be used inall types of physical therapy and rehabilitation.

FIG. 4 illustrates an example system for training a neural pathway. Someembodiments of the method disclosed in FIG. 3 and Examples 1 and 2 maybe performed using the system disclosed in FIG. 4. A system for traininga neural pathway may include an environment isolation device 400. Forexample, environment isolation device 400 may be a box configured toenable a subject to insert a specific anatomical component (e.g. a legor an arm) such that the environmental conditions affecting thatanatomical component are controlled (e.g. the level of light, wind,moisture, humidity, or other environmental conditions may be preciselycontrolled). Some embodiments may also include an optical 3D imagingmodality 410, neuro-cognitive imaging modality 430, and correlationengine module 420, wherein optical 3D imaging modality 410 may beoptically coupled to environmental isolation device 400 and configuredto capture static and dynamic optical image sets of a subject's anatomyinside the environmental isolation device. Neuro-cognitive imagingmodality 430 may be worn by the subject (e.g. an MEG or EEG), or may bein a separate location (e.g. a functional MRI device). Both optical 3Dimaging modality 410 and neuro-cognitive imaging modality 430 maytransmit imaging data sets to correlation engine 420, whereincorrelation engine 420 is configured to correlate geospatial imagingdata sets with neuro-cognitive imaging data sets, and calculate andcorrelate CPF and/or tCPF values to the imaging data sets such that, asa subject repeats a particular task, neural pathway variance and/oractivation response time is measured and used to calculate the CPFand/or tCPF values. Some embodiments may also include an internalimaging modality 440 configured to transmit internal imaging data (e.g.X-Ray, CT Scan, MRI, Ultrasound) to correlation engine 420 such thatinternal imaging data and optical 3D imaging data may be combined andused to render a virtual personification.

FIG. 5 is a flow chart illustrating a method for generating a predictivevirtual personification using cognitive and geospatial imaging analysis.An example of the method includes capturing a static geospatial imagingbaseline at step 510 and capturing a static neuro-cognitive imagingbaseline at step 520. For example, static geospatial imaging baselinemay include external imaging modalities such as digital cameras. In someexamples, the digital camera may be a mobile device camera and imagedata may be transmitted over the Internet or via wireless data networks.The static geospatial imaging baseline may also include internal imagingmodalities such as X-Ray, CT scanners, MRIs, Ultrasounds, or otherinternal imaging devices as known in the art.

Still referring to FIG. 5, the method may also include importinghistorical imaging data at step 530, calculating and storing CPF and/ortCPF values at step 540, and correlating data sets at step 550. Forexample, historical data may include archives of still photographs,videos, movies, television clips, medical records, medical images, orother available data specific to a subject that may provide static ordynamic anatomical evidence of the subject, including evidence of howthe subject may have previously performed certain tasks and/oractivities, and how the subject may have reacted to specific stimuli.The CPF values may be calculated from calculating trend variance ofneural pathway use in response to repeated exposure specific stimuliovertime, as observed using neuro-cognitive imaging. Alternatively, tCPFvalues may be calculated from calculating changes in neural pathwayactivation response times in response to repeated exposure specificstimuli over time, also as observed using neuro-cognitive imaging.

In other embodiments, empirical analysis of historical data may be usedto extrapolate a CPF and/or tCPF value from a subject's anatomicalmovement variance and/or response time changes over repeated performanceof the same task, activity, or stimulus response. Data correlation atstep 550 may include correlating internal and external geospatialimaging data sets with neuro-cognitive imaging data sets, CPF and/ortCPF values, and historical data to determine empirically therepeatability of a subject's anatomical movement responses, and/orphysically observable reactive response times, to a wide range of datapoints. For example, correlation data may be calculated using thecalculation methods illustrated in Equations 1 and 2.

Still referring to FIG. 5, examples of the method may also includegraphically rendering a predictive virtual personification from thegeospatial data at step 570. The graphical rendering may include imagesignal processing methods such as time mapping, warping, high dynamicrange to low dynamic range conversion, morphing, or other rendering andimage processing techniques as known in the art. The resultant renderedimage sets may depict a life-like/anatomically correct three dimensionaldepiction of the subject, and through image processing, the renderedthree dimensional depiction can simulate anatomically correct movement.For example, even though geospatial imaging may have not captured imagedata sets of a subject playing tennis, the rendering techniquesdescribed above may be used to extrapolate the anatomical movementsnecessary to swing a tennis racket and apply them to the rendering.Accordingly, a graphically rendered predictive virtual personificationcan depict the subject with anatomical accuracy and enable anatomicalmovement in response to artificial (previously unrecorded) stimuli.Thus, the method enables selecting a new stimulus at step 460 andapplying that stimulus to the predictive virtual personification, suchthat the predictive virtual personification may respond to the newstimulus based on the geospatial predictive algorithms described above,and in a manner that is either more or less predictable or randomdepending on the correlated stimulus specific CPF and/or tCPF.

EXAMPLE 3

In one example application of a method for generating a predictivevirtual personification, a deceased subject may be virtuallyre-personified for entertainment purposes (e.g. a predictive virtualpersonification of a deceased actor may be incorporated into a filmstarring that actor to avoid rewriting a script after the actor passesaway). In this example, the deceased subject may either create thepredictive virtual personification pre-mortem or post-mortem. If plannedpre-mortem, the process used may parallel the method disclosed in FIG.5. Initially, pre-mortem static geospatial and neuro-cognitive imagedata sets may be captured and stored in a database. Stimulus specificCPF and/or tCPF values may also be calculated and stored, and the datamay be correlated and rendered consistent with the methods disclosedherein. Thus, a predictive virtual personification representing thesubject pre-mortem is created. Then, post-mortem, image processingmethods may be applied to the predicative virtual personification toadjust for known aging variables (e.g. skin wrinkles may be added, hairand skin coloring adjusted, and movement algorithms adjusted to accountfor age). The predictive virtual personification may be entered into avirtual environment consistent with the post-mortem application (e.g. ifa movie, then the virtual environment would replicate the movie set forany given scene).

Still referring to Example 3, in an out-of-phase mode, the predictivevirtual personification may be configured to perform specific activities(e.g. swinging a golf club as part of a movie scene), but is unaware ofother dynamic conditions in the virtual environment, such as anotheractor or virtual personification moving in the scene.

Alternatively, still referring to Example 3, in an in-phase mode, thepredictive virtual personification may be configured to simultaneouslyperform activities and tasks while also responding to external stimuli,such as other subjects and/or virtual personifications within thevirtual environment. In this mode, the predictive virtualpersonification may use the correlated CPF and/or tCPF, and renderedimage data to interact in an anatomically and socially appropriatemanner within the virtual environment, and the reactions will reflectthe personality and anatomical mannerisms of the pre-mortem subject.

In some embodiments of this disclosure, a predictive virtualpersonification server may reproduce a donor subject's personality bygathering, defining, categorizing and/or mathematically establishingconditional probabilities relating visually and/or auditorily observablecharacteristics of the donor subject's personality, mood transitions,actions and activities. The predictive virtual personification server(PVP server) may then render an animated representation of the donorsubject—or a predictive virtual personification—that incorporates thesesame traits. Moreover, the predictive virtual personification may evolveover time, either by incorporating additional samples of observablevisual and/or auditory patterns related to those traits, and/or byapplying the CPF, as disclosed herein, to modify the predictive virtualpersonification's behavior over time (e.g., as the predictive virtualpersonification learns or adjusts to different environments).

In some embodiments, a personification consciousness validation model isdeveloped through the correlations of rhythmic entrainment, observablethrough neural and/or functional imaging, with visually and/orauditorily observable behavior patterns. Accordingly, a PVP may generatethe probability values relating a donor subject's observed response toemotions, moods, and personality interactions and responses coded in adialogue database.

As used herein, personification can mean the attribution of personalqualities and the representation of those qualities or ideas in or fromthe human structures and forms (e.g., behavioral patterns, emotions,tendencies, movement patterns, stimulus response patterns, etc.)

One embodiment of this disclosure is directed towards re-construction ofa predictive virtual personification capable of spontaneous interactionwith an environment that includes speech, comprehension, emotionalexpression through gestures, reactions, and tone of voice, and otherphysical behavior patterns.

In some embodiments, methods for generating a predictive virtualpersonification include identifying neural activity within a donorsubject's human brain by correlating said neural activity to visuallyand/or auditorily observable actions and/or responses to stimuli,whether those stimuli are external or internal to the donor subject.

These observable actions and/or responses may be attributable to aphenomenon known as rhythmic entrainment. As used herein, the termrhythmic is associated with identifying rhythmic activities in the coreof the brain. These rhythmic activities are markers for the variousconscious states. The term brainwave entrainment represents a donorsubject's brain's and body's responses to a given stimuli causingrhythmic entrainment of the brainwaves to measurable frequencies andobservable structure (e.g., through an MEG or EEG).

A donor subject's cognitive rhythmic entrainment processes may beobserved, captured, measured, and stored using imaging and analysissystems described herein. Rhythmic entrainments may include a donorsubject brain's neural activities generated in response to a stimulus.Rhythmic entrainment involves conditioning responses to that stimuluswithin a network of neurons inside the brain. The CPF, as describedherein, is thus related to and in fact describes this rhythmicentrainment process.

Similarly, actions, reactions, emotions and mood expressions are alsodependent from rhythmic entrainment. Moreover, these traits may all bephysically observable through interdependencies within the brain, suchthat a donor subject's response to any given stimuli may be colored bythat subject's current emotional state (a time specific state), as wellas the subject's personality, or learned behavior (colored bydeep-rooted rhythmic entrainment). Not only can this rhythmicentrainment be measured through neural and/or functional imaging (e.g.,through measurement of brain waves and/or brain function), but alsothrough physically observable characteristics such as micro-movements,twitches, reflexes, breathing patterns, rolling eyes, beads of sweat onthe brow, etc. These externalizations of a donor subject's internalneural activity are, thus, predictable within a certain degree ofuncertainty (related to the CPF, as described herein).

FIG. 6 is a diagram illustrating a system for generating a predictivevirtual personification, consistent with embodiments disclosed herein.As illustrated, a system for generating a predictive virtualpersonification includes an audio-visual (AV) data source 610, apredictive virtual personification (PVP) server 620, a data store 630,and an AV output device 640. AV data source 610 may be a historicalarchive or a live data source. For example, the historical archive maybe video, movie, television, medical imaging, or other historical videoand audio data. The data may initially reside in an analog format, inwhich case the system may also include an analog-to-digital conversionmodule that will convert the AV data to a digital format. The medicalimaging data may be from any of the medical imaging modalities disclosedherein. For example, the medical imaging data may include functionalbrain imaging data such as functional PET, functional MRI, MEG, or EEGdata.

The AV data source 610 may also be a live data source. For example, theAV data source may be one or more video cameras configured to capturevideo and audio of a donor subject. In some embodiments, the one or morevideo cameras are high-speed, ultra-high speed, high resolution, and/orultra-high resolution cameras. Multiple cameras may be configured tocapture video from multiple points of view as to capture threedimensional data, and reconstruct a three dimensional image of thesubject. In some embodiments, the multiple cameras may be configured tocapture images of a donor subject from multiple perspectives (forexample, by surrounding a subject with 6 cameras, each configured tocapture a 60 degree field of view). The AV source need not be a videocamera. For example, other optical imaging techniques may be used, suchas laser imaging. In some examples, the AV source is a 3D laser imagingsystem, such as a nanophotonic coherent imager. In other examples, theAV source may be a multi-array data capture device. Other AV sources maybe used as would be known in the art.

PVP server 620 may include a saliency recognition engine 622 and a PVPcorrelation engine 624. Saliency recognition engine 622 may beconfigured to receive one or more AV data sets from the AV data sourceand identify salient features within those data sets. For example, oneor more saliency regions of interest SROI's may be defined and observedby the saliency recognition engine. These features may be identifiedusing pattern recognition algorithms as known in the art. For example,SROI's may include a donor subject's face and/or aspects therein,including the cheek, mouth, nose, eyes, eyelids, brow, or ears. SROI'smay also include a donor subjects, shoulders, hands, arms, legs, torso,chest, abdomen, etc. The SROI need not be visual content, but insteadmay be audio content. For example, breathing sounds, or voice cadence,pitch, and/or volume.

In the examples whereby the SROI is video content, the saliencyrecognition engine 622 may use known saliency identification methods,along with pattern recognition algorithms, to identify and track theSROI within the video content, as differentiated from backgroundobjects. For example, the saliency recognition engine may identifygeospatial characteristics within pixels, or voxels in the case of athree dimensional data set, and determine that the geospatialcharacteristics match a pre-determined unique pattern that defines theSROI. Initially, individual SROI's may be manually defined through userinterface 650. For example, a user may freeze the video content andidentify one or more SROI's by highlighting/selecting the region with acursor, box, or free-form drawing tool. Alternatively, the SROI's may bepre-coded into the saliency recognition engine. In either case, thesaliency recognition engine then determines whether the SROI is anactual salient feature by comparing multiple image frame data-sets takenin sequence, and determining if the SROI moves independent of any of theother features in the background and/or the donor subject. Thesemovements, for each SROI, can be tracked over time to generate anSROI-specific saliency map, that can then be correlated to otherSROI-specific saliency maps, and/or triggering events. TheseSROI-specific saliency maps may be stored in data store 630.

PVP server 620 may also include PVP correlation engine 624. PVPcorrelation engine 624 may receive one or more SROI-specific saliencymaps from data store 630, as well as one or more event maps from datastore 630. For example, an event map may identify a specific time that astimulus event occurred and the specific type of stimulus event. Theevent map may be manually entered through user interface 650, may beautomatically identified as a first saliency peak in one or more of theSROI-specific saliency maps, or coded into the PVP correlation engine.For example, stimulus events may be reactions to a change anenvironment, such as an oncoming baseball, oncoming car traffic,physical contact from another person, etc. The stimulus event may alsobe a decision internal to the subject such as the decision to throw aball, hit a golf ball, run, walk, sing, etc. The stimulus even may alsobe an audio stimulus such as a question posed by another subject, or thesound of an oncoming car.

PVP correlation engine 624 may then analyze a SROI-specific saliency mapto correlate the movement of the SROI within the SROI-specific saliencymap with the stimulus event. Accordingly, the saliency map may displayseveral micro-movements, jitters, or macro-movements between the time ofthe stimulus event and the time of an active response. The activeresponse, for example, may be the swing of a bat at a baseball, swing ofa golf club at a golf ball, throwing of a baseball, etc. Other activeresponses are possible, even if subtle, such as an active response ofwalking up to a microphone and singing a note following the stimulusevent of the subject's decision to sing into the microphone. In someexamples, the stimulus event may be identified by first detecting theactive response, and then analyzing some or all of the SROI-specificsaliency maps to determine when the stimulus event occurred.

Saliency map patterns from the SROI-specific saliency maps for eachparticular stimulus event should be unique to a particular subject, andthus allow for automated recognition of that subject. In someembodiments, the PVP server may augment SROI-specific saliency map datawith CPF scores, or predicted CPF scores, to account for learningpatterns that may modify the SROI-specific saliency map (e.g., thesaliency map from a batters hands in the context of a batter swinging atan oncoming pitch may change slightly as the batter becomes morecomfortable through batting practice—this change may be estimated usinga CPF).

PVP correlation engine 624 may identify within each SROI-specificsaliency map the stimulus event and the active response, and then returnthe correlated SROI-specific saliency map to the data store.

PVP server may also include PVP rendering engine 626. PVP renderingengine 626 may receive subject dependent image data sets and audio datasets, and incorporate 2D and 3D rendering algorithms to render a 2D or3D animated representation of the subject. PVP rendering engine 624 mayalso receive correlated SROI-specific saliency maps from the data storeand apply those maps to the animated representation of the subject tocreate a predictive virtual personification. PVP rendering engine 624may also receive user input from user interface 650 to identify one ormore target stimulus events to present to the predictive virtualpersonification. PVP rendering engine 626 may then animate thepredicative virtual personification and apply the set of correlatedSROI-specific saliency maps that relate to the target stimulus events todirect the animation of the predictive virtual personification. Forexample, the target stimulus event may be a decision to start singing asong. The PVP rendering engine may then receive, for each SROI, thecorrelated SROI-specific maps related to the stimulus event wherein thesubject decided to start singing a song and apply those changes to thepixel and/or voxel maps generated by the PVP rendering engine to animatethe predictive virtual personification in the same way that the originalsubject would have reacted to the same stimulus. In some embodiments,the PVP rendering engine may weight each of the SROI-specific mapsaccording to a CPF, such that as a subject, or predictive virtualpersonification, becomes more comfortable reacting to a particularstimulus, the SROI-specific maps compress such that the time shortensbetween the stimulus event and the active response.

The PVP server may also include environment rendering engine 628.Environment rendering engine may use known two dimensional and threedimensional rendering methods to calculate a virtual environment for thepredictive virtual personification. For example, the virtual environmentmay be a movie set, buildings, a baseball fields, a stage, or otherenvironments in which the predictive virtual personification is meant tointeract. The environment rendering engine may render the environmentusing archived image data from a real world environment and using knowncomputer graphic imaging (CGI) methods. In some embodiments, environmentrendering engine does not calculate a virtual environment, but insteadreceives video and audio data from a real environment and communicatesthat data to the PVP rendering engine such that the predictive virtualpersonification may interact in a real environment. In some embodiments,multiple, distinct predictive virtual personifications taken fromdifferent subjects may interact within a single environment.

The system may also include AV output device 640. For example, AV outputdevice may be a digital video converter configured to store digitalvideo to a storage media. AV output device may also be an AV projector,or a holographic image projector to project a predictive virtualpersonification into a real environment using a hologram or videoprojection. AV output device may also be a digital media screen, such asa video monitor, TV monitor, mobile device display, or other display. AVoutput device may also be a social media network, such that a predictivevirtual personification may be uploaded to a user's social media page.

The system may also include user input device 650. The user input devicemay enable a user to input predictive virtual personification parametersto facilitate the generation of the predictive virtual personification.For example, user input device may be a computer console, a mobiledevice, a tablet device, or other input device. A user may use the userinput device to input parameters such as stimulus event maps thatinclude the time of a stimulus event and/or the time of active responseto the stimulus, correlation with an SROI-specific saliency map. Theinput parameters may also include labels, descriptions, and all typesassociated with specific stimulus events. For example, types of stimulusevents and categories such as reactions to approaching objects,responses to audio cues, reactions to stationary visual cues, etc.Labels may include identification of specific stimulus events such asswinging back at an oncoming baseball, swing a golf club at a stationarygolf ball, approaching or grabbing a stationary microphone stand inpreparation of starting a song, reaction to loud applause, etc.

Any stimulus event in which a donor subject interacts with anenvironment may be incorporated into the system through the capture ofapplicable saliency data, correlation of the stimulus event with aplurality of SROI-specific saliency maps, and categorization of thestimulus with a label, description, and type. In some embodiments,saliency recognition engine 622 may automatically recognize and/orsuggest one or more labels, descriptions, and types for an observedstimulus event based on automatic recognition of saliency patternswithin one or more observed SROI's. For example, saliency recognitionengine 622 may observe an SROI encompassing a subjects hand throwing abaseball, match the saliency pattern observed to a known saliencypattern of a baseball being thrown, and a label the stimulus eventaccordingly.

FIG. 7 is a diagram illustrating a method for generating and using apredictive virtual personification. The method includes receiving one ormore audiovisual (AV) data sets of a donor subject at step 705. Forexample, the AV data set could be real-time capture from any of the AVdata sources described with respect to FIG. 6 above, as well ashistorical AV data sets. The AV data sets will be associated with adonor subject performing a specific task, or reacting to a stimulusevent. The stimulus event may be an external stimulus event, such as itapproaching baseball, it approaching automobile, and anticipatedphysical contact by another person, or any other external stimulusevents disclosed herein, or that would be known to one of ordinary skillin the art. This is event may also be an internal stimulus event, suchas a decision to perform a task. For example the task may be swing agolf club at a golf ball, or approaching a microphone to sing, or anyother volitional task in which the subject might participate.

The method for generating and using a predictive virtual personificationalso includes generating a set of physio-emotional characteristics atstep 715. For example, the saliency server may generate the set ofphysio-emotional characteristics as one or more SROI specific saliencymaps, consistent with the methods disclosed herein. The method forgenerating and using a virtual personification may also includerendering a geospatial animation of the subject at step 725. Forexample, historical geospatial imaging of the donor subject taken frommultiple perspectives may be compiled within PVP rendering engine 626.PVP rendering engine 626 may then use known CGI rendering methods togenerate either a two-dimensional or three-dimensional animation of thedonor subject. In some embodiments, the animation of the donor subjectmay be a realistic depiction of the donor subject. In other embodiments,the animation of the donor subject may be a cartoon of the donorsubject, or an altered version of the donor subject.

The method for generating and using a predictive virtual personificationmay also include rendering physio-emotional characteristics of thesubject at step 735. For example, the physio-emotional characteristicsassociated with a donor subject may be stored in data store 630. Thesephysio-emotional characteristics are correlated SROI specific saliencymaps associated with various stimulus events. Data store 630 may alsostore a database organizing various stimulus events by label and type.In some embodiments, the saliency data collected from the donor subjectmay only relate to a subset of the available stimulus event types and/orlabels known to the predictive virtual personification system. However,PVP rendering engine 626 may extrapolate the correlated SROI specificsaliency maps generated from the AV data sets of the donor subject tocreate rendered SROI specific saliency maps that cover a complete set ofprobable stimulus events. For example, PVP rendering engine 626 maycompare correlated SROI specific saliency maps for one donor subject inreaction to a subset of stimulus events to other subjects reaction tothe same types of stimulus events to identify closely matching profiles,and differences within those profiles between the one donor subject andthe other subjects. The PVP rendering engine may then use thosedifferences to extrapolate to the one donor subject a complete set ofrendered SROI specific saliency maps for stimulus event data collectedfrom the other subjects. Accordingly, PVP rendering engine 626 mayeither recall, or calculate on-the-fly a set of rendered SROI specificsaliency maps approximating how a donor subject would react to any ofthe stimulus events and/or stimulus event types known to the predictivevirtual personification system.

In some embodiments, more than one set of correlated SROI specificsaliency maps will be captured and stored for a specific stimulus event.Thus, a donor subject may react differently, with some slight variancesto a specific stimulus event. The rendering of the physio-emotionalcharacteristics may also include applying a Bayesian probabilityfunction to determine which set of correlated SROI specific saliencymaps to apply in response to a specific stimulus event, in light of thehistoric response pattern a donor subject may have had to similarstimulus events.

Rendering the physio-emotional characteristics of the subject at 735 mayalso include adapting correlated and/or rendered SROI specific saliencymaps using a CPF function. For example, as a donor subject is repeatedlyexposed to the same stimulus event, the donor subject's natural reactionto estimates of may change. For example, the speed of any reaction tothe stimulus event may increase such that a pattern linking anypreprocessing and/or preplanning by the donor subject in relation to thedonor subject active response to the stimulus event may become morepredictable, more repeatable, and faster, following a CPF function.Thus, the CPF function may be applied to the rendered or correlated SROIspecific saliency maps, increasing the probability that any given mapmay be applied in response to a specific stimulus event, in factaltering that map by shortening the time between stimulus event andactive response.

The method for generating and using a predictive virtual personificationmay also include rendering environment at step 745. For example, theenvironment may be any setting applicable to the donor subject. Forexample, the environment could be a baseball field, a golf tee box, astage of the music concert, a couch in the living room, or any otherplace that a person might find himself or herself. Environment renderingengine 628 may use known CGI methods to render the environment based onhistorical AV data of similar environment stored in data store 630.Alternatively, in some embodiments, environment rendering engine 628renders an environment based on real-time AV data sets being capturedthrough AV source 610 such that a real-time environment may be providedto the predictive virtual personification.

The method for generating and using a predictive virtual personificationmay also include applying the geospatial animation of the donor subjectwith the physio-emotional characteristics at step 755. For example, thepredictive virtual personification may be rendered within the renderedenvironment and exposed to one or more stimulus events. As saliencyrecognition engine recognizes the stimulus events, or the stimulusevents are either pre-populated or populated in real time into the PVPserver through the user interface, the PVP rendering engine can predicthow the donor subject would have reacted to the stimulus event, and mayapply a set of rendered SROI specific saliency maps to animate thepredictive virtual personification within the rendered environment. Incases where the rendered environment is drawn in real time from a realenvironment, the predictive virtual personification may be projectedinto the real environment through AV output device 640. In cases wherethe rendered environment is generated from historical or animated datasets, both the rendered environment and predictive virtualpersonification may be output through AV output device 640.

FIG. 8 is a flow chart illustrating a method for identifying and storinga donor subject's physio-emotional characteristics. For example, themethod for identifying and storing the donor subject's physio-emotionalcharacteristics may include receiving saliency regions of interest(SROIs) at step 805. The SROI's may be any visually or auditorilyobservable feature of the donor subject that may exhibit observablecharacteristics, whether macro or micro, in response to the stimulusevent. For example, the SROI may be a donor subject's brow, and theobservable characteristic may be a twitch or formation of a bead ofsweat in response to concentrating on an oncoming baseball pitch. Inanother example, the SROI may be a donor subject and the observablecharacteristic may be rapid blinking in response to a decision to walkout onto a stage in front of an audience. The SROI may be auditory aswell, for example, the change in the pitch of a donor subject's voice inresponse to big asked a question to which the donor subject does notknow the answer.

The number of possible SROI-stimulus event pairs is vast. However,embodiments of this disclosure may use a subset of SROI-stimulus eventpairs, and categorize the stimulus events by type to enableextrapolation by PVP rendering engine 626 according to methods disclosedherein. Accordingly, PVP rendering engine 626 may extrapolate a donorsubject's anticipated reaction to similar types of stimulus events basedon the donor subject's reaction to an actual stimulus event of the sametype. Moreover, the receiving saliency regions of interest at step 805may include receiving previously un-catalogued saliency regions ofinterest has to expand an SROI library. Just as with stimulus events,each new SROI may include a label, a description, and a type, whereinthe type may indicate a genus or category of SROI. For example, the typeof SROI may include SROI's within the subjects face and the speciesSROI's may include the cheek, the brow, the eyelid, the mouth, etc.

The method for generating physio-emotional characteristics may alsoinclude receiving AV data sets and corresponding event maps at step 815.For example, the AV data sets may be any of the data sets generated byAV data source 610 is disclosed herein. The event maps may be time linesindicating the time of the stimulus event, and the corresponding time ofan active response by the donor subject. For some stimulus events, thestimulus event time and the active response time may both be observable,and thus the event that may be automatically calculated. For example,the stimulus event could be an oncoming baseball pitch, the SROI couldbe the donor subject's hand, and the active response could be moving thehand in such a fashion as to the bat towards the oncoming pitch. Forother stimulus events, either of the stimulus event time or the activeresponse time may not be observable. For example, the stimulus event maybe an internal decision to walk towards a microphone and begin to sing asong. In such a case, the active response will be walking towards themicrophone, but the seamless event, the actual decision, may not bevisually observable. However, an event map may be manually enteredindicating the time that the stimulus event took place, or in somecases, the stimulus event may be observable through the use of otherimaging modalities. For example, an event that may be captured usingfunctional brain imaging to identify when the stimulus event took place.

The method for generating physio-emotional characteristics may alsoinclude generating, with the saliency recognition engine, SROI specificsaliency maps at step 825. Generating a SROI specific saliency map mayinclude identifying one or more SROI's using saliency recognition and/orpattern recognition methods. For example, identifying a moving cheekwithin AV data set may include recognizing the pattern of the cheek,and/or identifying of the cheek with respect to the background. In someembodiments, ultrahigh resolution, or ultrahigh frame rate cameras maybe used to capture very fast and/or very small movements at an SROI. TheSROI specific saliency map, then, may be a plot in one or more spatialdimensions illustrating the movement of the SROI, or one or more thepixels/voxels therein, with respect to time. Multiple SROI specificsaliency maps may be captured for a single SROI, as well as for aplurality of SROI's, and stored in data store 630.

The method for generating physio-emotional characteristics may alsoinclude correlating, with a PVP correlation engine, the event maps tothe SROI specific saliency maps at step 835. Accordingly, each SROIspecific saliency map may be converted to correlated SROI specificsaliency map in which both stimulus event time and active response timeidentified on the map. In some embodiments, the stimulus event time orthe active response time may be visually observable or apparent withinone SROI specific saliency map, but not within a matter SROI specificsaliency map. For example, the stimulus event of an oncoming baseballpitch may be observable at an SROI of the donor subject's brow thatraises and lowers multiple times is a better prepare swing at the pitch.But then, as a better actually swings at the pitch the SROI of the handsmoving will clearly indicate the active response of swinging, but thebrow may not move. Thus, the PVP correlation engine cross correlate SROIspecific saliency maps to extrapolate full event maps from one SROIspecific saliency map to another. This type of extrapolation isillustrated in the description of FIG. 10 below.

The method for generating physio-emotional characteristics may alsoinclude compiling the correlated SROI specific saliency maps within aphysio-emotional response characteristics database at step 845. Thephysio-emotional response characteristics database may be stored in datastore 630, and may provide an archive of correlated and rendered sets ofSROI specific saliency maps for a given donor subject in response to aset of stimulus events and stimulus event types.

In some embodiments, the PVP rendering engine 626 may employ a learningalgorithm to analyze correlated SROI specific saliency maps and learn adonor subject's physio-emotional characteristics over time. For example,the donor subject's physio-emotional characteristics may be dynamic andchange based on learned traits through interaction with one or moreenvironments (either rendered or real). PVP rendering engine may use adeep learning algorithm to organize and parse a set of correlated SROIspecific saliency maps to determine a donor subject's likely response todifferent types of stimuli. For example, in one embodiment, the deeplearning algorithm may calculate deep neural networks based on thecorrelated SROI specific saliency maps using statistical probabilityfunctions relating types of stimuli to predicted responses. PVPrendering engine 626 may also use a renormalization group process, usingthe SROI specific saliency maps as inputs, to determine a donorsubject's likely response to different types of stimuli. These samevirtual learning techniques, as well as others known in the art, may beapplied to the PVP rendering engine, as well as other modules of PVPserver 620, such as saliency recognition engine 622, PVP correlationengine 624, and environment rendering engine 628.

In one embodiment, organizing and parsing a set of correlated SROIspecific saliency maps to determine a donor subject's likely response todifferent types of stimuli may be framed as an unsupervised learningproblem. Accordingly, the PVP server 620 may employ a deep learningalgorithm to interpret the unlabeled data. For example, a single donorsubject may have many SROIs, ranging from a few as illustrated in FIG.9, to hundreds, thousands, or millions. Each SROI may be evaluated manytimes to create many SROI specific saliency maps (e.g., tens, thousands,millions, or hundreds of millions of SROI specific saliency maps perSROI), which in turn may be correlated to many, if not all of the otherSROI specific saliency maps. Given the large size of this data set,using unlabeled data to enable a deep learning data analysis may beefficient.

In addition, deep belief networks may provide deep structure that can betrained in an unsupervised manner, e.g., using neural networks that arepartially trained by unsupervised learning. Data processed using thisdeep learning algorithm may be depicted in a distributed representation.The distributed representation to observe data (i.e., relationshipsbetween the correlated SROI specific saliency maps over time as afunction of the subject donor's physio-emotional state (i.e., a mood,level of rest, level of stress, performance, etc.), as well as a triggerstimulus event. Interactions of many different factors may berepresented and abstracted through a multi-layer deep learning analysis.As such, a set of historical SROI specific saliency maps, illustratinggeospatial movements for each of a plurality of preselected SROIs (i.e.,observable features of a donor subject) may form a historical saliencymap space that includes multiple dimensions, including a triggerstimulus dimension (e.g., a set of historical SROI specific saliencymaps that correlate to specific types of trigger stimuli), as well asphysio-emotional characteristics such as mood, stress level, health,rest state, performance, etc. The historical saliency map space may alsoinclude a time dimension and a CPF dimension, such that a donorsubject's observable reactive response to a particular trigger stimulusevent, or type of trigger stimulus event, may change over time incorrelation to a CPF, as the reactive response becomes learned orforgotten. As such, a renormalization group transformation may beapplied to the historical saliency map space to transform the historicalsaliency map space to a predictive saliency map space. Then, frompredictive saliency map space, a particular set of predictive SROIspecific saliency maps may be extrapolated by selecting a triggerstimulus event, physio-emotional state or characteristic, or otherparameters/dimensions as defined in the predictive saliency map space.

For example, the deep learning algorithm may actually comprise a set ofalgorithms (e.g., non-linear transformations) to model high-levelabstractions in the data using model architectures and/or neuralnetworks that can learn and recognize patterns, such as patternrecognition algorithms as known in the art. One example algorithmemployed by the PVP Server 626 is a deep learning algorithm that employsvariational renormalization groups that can operate on structured andunstructured data sets.

Context or context aware computing for purposes of this invention can beprogramed to sense the physical environment of the donor, and adapt thedonor's behavior accordingly. Context-aware systems are a component of aubiquitous computing or pervasive computing environments. Threeimportant aspects of context are: who the donor is, where the donor is;who is the donor with; and what resources are available. Althoughlocation is a primary capability, location-awareness does notnecessarily capture things of interest that are mobile or changing.Context-aware can include nearby people, devices, lighting, noise level,network availability, and even the social situation. Context awarenesscan be developed using the key programing elements defined and referredto herein to develop, identify and to integrate key elements, such asenvironments, situational statuses, donor self and predictive awarenessof and by the donor.

FIG. 9 is a diagram illustrating an example set of saliency regions ofinterest within a donor subject. The SROI's illustrated are just anexample of the possible SROI's that may be tracked. As illustrated, agenus of SROI's, 910, may be located within a donor subject's facialarea, including species SROI's 912 (the eye lid, cheek, mouth, neck,etc.). Similarly, another genus of SROI's, 916, may be located within adonor subject's arm, including hand 918 and fingers 920. Other SROI'smay be tracked, as would be appreciated by one of skill in the art.

FIGS. 10A-10C are charts illustrating a relationship between movement inone dimension of multiple saliency regions of interest as correlatedwith a stimulus event and a corresponding action event. For example,FIG. 10A illustrates movement along the x-axis for an SROI over a giventime period, t. Thus, plot 1010 shows movement following a stimulusevent at time 1012 culminating in a larger movement that active responsetime 1014. Stimulus event time 1012 and active response time 1014 makeup an event map that is correlated to this SROI specific saliency map.Using a high frame rate camera, and/or a high-resolution camera, smalland fast movements, jitters, or micro-movements may be tracked betweenthe stimulus event and active response.

Thus, for a given SROI, a donor subject's habitual movements andreactions leading up to an actual active response may be observedcorrelated with the stimulus event. The same map then may be applied topredict either how a donor subject will react to a similar stimulusevent in the future, or to identify what a stimulus event was that mayhave caused a particular reaction. Moreover, other physio-emotionalparameters may be compiled correlated with each SROI specific saliencymap, such as, a donor subject's emotional state. For example, multipleas ROI specific saliency maps may be compiled for a donor subjectdeciding to start singing a song into a microphone, but for some of themaps, the subject may be nervous, upset, angry, happy, sad, etc. each ofthese emotional states may be entered via user input upon a first datacollection, but may later be identified by matching the captured SROIspecific saliency map with a previously observed SROI specific.

Moreover, the subjects emotional state may be incorporated into apredictive virtual personification, such that the predictive virtualpersonification's anticipated responses to any given stimulus event maydepend upon the current emotional state of the predictive virtualpersonification. This emotional state may be entered through a userinterface, hardcoded into the project virtual personification,participated in response to previous stimulus events.

FIGS. 10B and 10C illustrate SROI specific saliency maps 1020 and 1030,respectively, captured for different SROI's, but under the same stimulusevent 1012 as illustrated in FIG. 10A, and culminating in the sameactive response 1014. As demonstrated, the stimulus event may be moreeasily observable and one SROI specific saliency map from another, asmay the active response. However, the event map of times 1012 and 1014may be correlated across each SROI specific saliency map compiled fromdata captured by observing each discrete SROI.

In some examples, a predictive virtual personification may be generatedand populated into a real or virtual environment. During interactionwith that environment, AV data may be captured, and the processesdisclosed herein for analyzing and creating a predictive virtualpersonification may be run for a second time, to create a secondderivative predictive virtual personification. Through the addition ofnew stimuli and user input, multiple derivative virtual personificationsmay be generated, each engineered to behave slightly differently basedon experiences and learning during exposure to real or renderedenvironments. For example, a first level virtual personification may bebased on a donor subject that speaks English and conforms with Americanculture. That virtual personification may be exposed to French languageand culture in a rendered or real environment. The new language andbehavior sets may be adopted by the first level virtual personificationover time. A second level virtual personification may then be captured,either from the original donor subject, or from the first level virtualpersonification. This process could be repeated such that a single donorsubject may have many virtual personifications that learn differentbehaviors based on the environments in which those virtualpersonifications interact. The same principles for learning, disclosedherein, may be applied to those derivative virtual personifications,just as they were applied to the original donor subject. As such, thederivative virtual personifications may each have the same rootbehavioral characteristics, but actual expressions and behaviors mayvary, just as they might, for example, for genetically identical twinsthat are exposed to different environments.

Embodiments of this disclosure combine and correlate AV data of a donorsubject (e.g., a human subject) to the donor subject's neural activity,including behavioral end emotional states and reactions to stimuli. Someembodiments analyze cognitive plasticity and compare that analysis witha donor subject's observable actions. AV data sets may be gathered andanalyzed, and then compiled and/or rendered into a predictive functioncapable of virtually anticipating a donor subject's behavior andemotional states. As used herein, consciousness means the quality orstate of awareness, or, of being aware of an external object orsomething within oneself. For example, sentience, awareness,subjectivity, the ability to experience or to feel, wakefulness, havinga sense of selfhood, and the executive control system of the mind.

Some embodiments of the disclosure provide a method for training aneural pathway. An example method of training a neural pathway mayinclude capturing, with a geospatial imaging modality, one or moregeospatial imaging data sets, capturing, with a neuro-cognitive imagingmodality, one or more neuro-cognitive imaging data sets, and applying astimulus. The method may further include calculating, with a computerprocessor, a stimulus specific cognitive plasticity factor (CPF) andre-applying the stimulus.

Some of the detection devices and methods disclosed herein rely onphysically observable signaling of a human subject that is linked to a“readiness potential.” As used herein, a readiness potential is anon-volitional component of a donor subject's volitional responses,actions, or movements. For example, a generally repeatable,subject-unique physical movement or set of movements will typicallyprecede a donor subject's volitional response, action, or movement. Thatis, before a donor subject executes a conscious decision to move,muscles in the body will have already started flex. This phenomenon is apre-cognitive function referred to herein as a readiness potential, andin many cases can be visually observable, albeit high resolution and/orhigh frame rate video capture equipment may be required. And, the CPF,as disclosed herein, may affect the length of the time-frame in whichthe readiness potential is observable for a particular subject andaction.

For example, initial neural signaling related to a reaction, response,or decision to act or move can drive from other parts of the brain tothe premotor cortex and the motor cortex, where detailed instructions toflex specific sets of muscle fibers are generated and transmitted downthe spinal cord and to the related muscles. In many cases, a decision tomove occurs prior to the individual becoming cognitively aware of thedecision or how to execute the decision. The actual volitional (ornon-volitional, as the case may be) action is pre-determined in themotor cortex without requiring the individual to develop, or think aboutthe specific instructions. For example, an individual does not typicallythink about which muscle fibers to flex in the leg to make the leg moveforward and walk—instead, the individual just decides to walk. Theindividual may be even less aware of the readiness potential—butnevertheless, the body may still react during the readiness potentialtime-frame through twitches, eye-blinks, brow raises, breathing cadencechanges, quivers, micro-movements, etc. Many of these movements will bephysically observable during a short period of time between when theindividual decides to act (or reacts to a stimulus) to the time that theactual active response occurs.

As describe the CPF is a measure of precognitive, awareness, andconsciousness functions including the readiness potential describedabove. As may be understood to some of skill in the art, neural activityduring the readiness potential period, and even prior to the readinesspotential period is the result of neurochemical processes, and affectedby interactions of molecules, and even atoms, on a quantum level. Theneural activity that occurs during this readiness potential time framecan be observed using neural imaging, functional imaging, and/oraudiovisual imaging of a donor subject. For example, physical movements,caused by the neural activity during the readiness potential period maybe observable by high frame rate, high resolution, ultrahigh frame rate,and/or ultrahigh resolution video capture equipment. This observablemovement map may reflect how an individual donor subject reacts to anyvariety of stimuli, including internal stimuli that themselves are notobservable (e.g., decisions made internally by the donor subject).

Each donor subject's neural staging processes, of which the readinesspotential is one, activates specific neural circuits and synapses.Embodiments disclosed herein are directed at detecting these processesthrough indirect observation (i.e., by observing outward physicalmovements) to identify subject-dependent “hyper temporal” plasticity andrelated hyper temporal preparation states.

For example, this hyper temporal, early temporal, mid temporal, andlater temporal preparation and initial execution states can be from afew milliseconds to over 500 milliseconds and those when optionallyseparated and analyzed can be combined with other identifiable forms ofthe subjects physical anatomy in both microscopic and macroscopiclevels.

The movement map may also be unique to the individual donor subject, andmay vary slightly based on environmental parameters, as well as based oninternal parameters such as the donor subject's emotional state. Forexample, an angry donor subject may behave differently than a happydonor subject. In either case, a compilation of movement maps for anyspecific individual target will be unique to that donor subject, justlike a finger print. These observable movements and tendencies duringthe readiness potential period are also recognizable to third-partiesand help define a person's “essence.” The outward appearance ofindividual's movements, actions and activities is the direct reflectionof the individual's internal consciousness. Similarly, changes in theseobservable movements in response to the same or similar stimuli mayindicate learning, or may be used to diagnosis developmental,psychological, or mental disorders. These movements can be observed, andpredictably recreated using some of the embodiments disclosed herein.

Cortical organization, especially for the sensory systems, is oftendescribed in terms of cortical maps. One of the fundamental principlesof how neuroplasticity functions are linked to the concept of synapticpruning, the idea that individual connections within the brain areconstantly being removed or recreated according to how the neurons areused. If there are two nearby neurons that often produce an impulsesimultaneously, their cortical maps may become one. This idea also worksin the opposite way, i.e. that neurons that do not regularly producesimultaneous impulses will form different maps. When a reaction to astimulus event is cognitively associated with reinforcement, itscortical representation is strengthened and enlarged. This effect ismeasurable through the CPF defined herein.

For example, the recognition of faces is an important neurologicalmechanism that an individual uses every day. Faces express a significantamount of information that guides our social interactions, reactions,and emotions that play a large role in our social interpretations andinteractions. The perception of a positive or negative emotion on a facecan significantly affect the way that an individual perceives, processesand reacts to that face. For example, a face that is perceived to have anegative emotion can be processed in a less holistic manner than a facedisplaying a positive emotion. The neurological mechanisms responsiblefor face recognition are present by age five. Research shows that theway children process faces is similar to that of adults, but adult'sprocess faces more efficiently. The reason for this has been attributedto advancements in memory and cognitive functioning that occur with age.

Some embodiments disclosed herein detect saliency, and saliencypatterns. A saliency recognition engine may be used to find, recognize,and track salient regions of interest (SROI) within a donor subject. Insome examples, a wide field of view capture device may be used inconjunction with a saliency recognition engine to monitor more pixels atthe same time. High speed processing within the saliency recognitionengine, may be required in connection with high frame rate and ultrahighframe rate cameras to detect SROI's in real time.

As used herein, an event-related potential (ERP) is the measured brainresponse caused by a sensory, cognitive, or motor event within a donorsubject. For example, it is any stereotyped electrophysiologicalresponse to a stimulus. Some embodiments may be directed towardsdetecting an event related potential (ERP) component elicited in theprocess of decision making. The may reflect processes involved in adonor subject's stimulus evaluation or categorization. For example, theERP may be recorded by electroencephalography (EEG), as a positivedeflection in voltage with a latency (delay between stimulus andresponse) of roughly 250 to 500 ms. The signal is typically measuredmost strongly using electrodes that cover the parietal lobe. Thepresence, magnitude, topography and timing of this signal may be used tocharacterize a cognitive function involved in a donor subject's decisionmaking processes. An ERP may also be measured using MEG. For example, insome embodiments, a donor subject may be exposed to the same stimulusevent repeatedly, and MEG and EEG data may be captured to characterizean ERP response to the stimulus. This may be repeated for many stimulito characterize a set of ERP curves unique to that individual. The datamay be correlated with captured AV data to correlate external movementswith the ERP wave.

Wide amplitude noise (such as eye blinks or movement artifacts) areoften several orders of magnitude larger than the underlying ERPs.Therefore, trials containing such artifacts should be removed beforeaveraging. Artifact rejection can be performed manually by visualinspection or using an automated procedure based on predefined fixedthresholds (limiting the maximum EEG amplitude or slope) or ontime-varying thresholds derived from the statistics of the set oftrials.

Compared with behavioral procedures, ERPs provide a continuous measureof processing between a stimulus and a response, making it possible todetermine which stage(s) are being affected by a specific experimentalmanipulation. Another advantage over behavioral measures is that theycan provide a measure of processing of stimuli even when there is nobehavioral change. However, because of the significantly small size ofan ERP, it may take a large sample size to accurately measure itcorrectly. ERPs provide temporal resolution of 1 ms or better. However,the spatial resolution for fMRI or PET exceeds the spatial resolutionfor ERP.

An alternative approach to saliency recognition, as described above, ispremised on “pure surprise” is to use a full surprise algorithm. Fullsurprise algorithms employ additional processing on the features in eachframe of the video and create statistical models that describe thescene. If anything unexpected happens, the surprise algorithm is able toreturn the location of the happening. A Bayesian framework may be usedto calculate a saliency map. Such an algorithm may use the KL distancebetween the prior and posterior as the measure of surprise. Because ittakes the entire history of the scene into account, it exhibits a muchlower false alarm rate than that of a system that exclusively usessaliency. One of skill in the art would appreciate that other saliencyrecognition engines may be used.

In some embodiments, characteristics are captured and correlateddefining a donor subject's body-brain-mind (BBM) from AV, neural, and/orfunctional imaging data sources. For example, anatomical imagingincludes using available historical records of the donor subject and/orreal-time data capture of salient elements of the donor subject and/orclose family members. The developing of general, and or detailed andcomplete image captures of uniquely identified and specific anatomical,biological, neurological detailed targets of the subject can take placein either real time or from historical imagery data bases, includingcomplete anatomical, medical, and neural/consciousness cognitive datacaptures. For example, CT scans, MRI, MEG, Ultrasound imaging oftargeted specific anatomical features and overviews of the subjectsunique feature and functional elements. This data can then be analyzedand/or combined for incorporation into broadcast ready materialscontent.

In some examples, temporal or time-stamped historical references arecombined with the real-time developed predictive virtualpersonification. The historical data may also or alternatively becombined with data captured from a similar, or related “stand in”subject who may emulate the donor subject. The data may then bereconstructed and rendered into personality and behavioralcharacteristics engrained in a virtual representation/likeness of thedonor subject—a predictive virtual personification—such that thepredictive virtual personification can interactively work, think, moveand act in optionally the same, similar or anticipated forms of thedesignated individual human being in the virtual electronic world ondemand. The predictive virtual personification may interact within avideo, film, television, real world environment, or social mediaenvironment. In some embodiments, the predictive virtual personificationmay be used in a medial environment for administering therapy to aloved-one related to the deceased donor subject. In other embodiments,the predictive virtual personification may serve as a way that distantrelatives can interact more closely and more often within a social mediasite, or using display and image capture capabilities on a mobiledevice, for example.

FIG. 11 is a diagram illustrating an example of an AV data capturedevice used to capture data used to generate a first level virtualpersonification from a donor subject. The AV capture device may includedigital video cameras 1110 mounted on rail 1120. For example, thedigital video cameras may be standard digital video cameras available inthe field. In some examples, digital video cameras 1110 are high framerate, high resolution, ultrahigh frame rate, and/or ultrahigh resolutioncameras. In some examples, digital video cameras 1110 may incorporatelaser imaging systems, including interferometric systems. Digital videocameras 1110 may be positioned on rail 1120 such that they are aimed atdonor subject 1150. Digital video cameras 1110 may also be repositionedand locked in place in any position along rail 1120 such that varyingangles and perspective fields of view are possible. The separation ofvideo cameras 1110 along rail 1120 enables capture of differentperspective angles of the same donor subject at the same time, enabling3D image reconstruction. Digital cameras 1110 may also be mounted onrail 1120 using motorized servos, or other automated technology toautomatically reposition to the cameras to different locations along therail. The video cameras also include audio capture devices 1112. Audiocapture devices 1112 may be standard audio microphones, or may bedirectional microphones, or high amplification microphones. Rail 1120 ismounted on support structure 1140, which may be oriented in a differentplane from rail 1120 such as to avoid interfering with the field of viewof video cameras 1110. This AV device is illustrated for exemplarypurposes only. One of ordinary skill in the art would appreciate thatmany other AV capture devices may be used.

In one example data capture system consistent with embodiments disclosedherein, a heart rate variability (HRV) sensor is included. HRV datacaptured using the HRV sensor may be correlated with other data (e.g.,AV data, SROI specific saliency maps, CPF, etc.) to further identifypatterns relating to predictable biophysical behaviors of a donorsubject. For example, the sympathetic and the parasympathic nervoussystem control the speeding up and slowing down for optimumcardiovascular activities. Thus, HRV may be measured and analyzed incomparison to EEG data using correlation analysis algorithms disclosedherein (e.g., a deep learning algorithm). Using HRV in concert with theother data capture technologies disclosed herein, a PVP server (i.e.,PVP server 626 from FIG. 6) can determine, analyze, and store a donorsubject's activity state emersion status, adaptive flexibility,innovation status and degrees, physio-emotional characteristics,controlled actions and discipline, cognitive functions and cognitiveawareness, environment and situational awareness, stress levels, and thechanges to each parameter in response to a trigger stimulus. Given thatthe cardiovascular system, and the heart specifically, is closelyconnected to the brain from a neurological perspective, HRV data can addan added dimension to identifying and predicting behavior of a donorsubject. By repeating this process over a wide range of conditions suchas time, space or environments, and donor's variability's and exposurefactors to stimuli and emotional states the donor's individualbio-physical and cognitive signatures (e.g., a CPF) can be analyzed,stored, archived, and associated with a virtual representation of thedonor subject (e.g., a rendered animation of the donor subject).

FIG. 12 is a diagram illustrating a method for generating and using apredictive virtual personification. The method includes receiving one ormore audiovisual (AV) data sets of a donor subject at step 1205. Forexample, the AV data set could be real-time capture from any of the AVdata sources described with respect to FIG. 6 above, as well ashistorical AV data sets. The AV data sets will be associated with adonor subject performing a specific task, or reacting to a stimulusevent. The stimulus event may be an external stimulus event, such as itapproaching baseball, it approaching automobile, and anticipatedphysical contact by another person, or any other external stimulusevents disclosed herein, or that would be known to one of ordinary skillin the art. This is event may also be an internal stimulus event, suchas a decision to perform a task. For example the task may be swing agolf club at a golf ball, or approaching a microphone to sing, or anyother volitional task in which the subject might participate.

The method for generating and using a predictive virtual personificationalso includes generating a set of physio-emotional characteristics atstep 1215. For example, the saliency server may generate the set ofphysio-emotional characteristics as one or more SROI specific saliencymaps, consistent with the methods disclosed herein. The method forgenerating and using a virtual personification may also includerendering a geospatial animation of the subject at step 1225. Forexample, historical geospatial imaging of the donor subject taken frommultiple perspectives may be compiled within PVP rendering engine 626.PVP rendering engine 626 may then use known CGI rendering methods togenerate either a two-dimensional or three-dimensional animation of thedonor subject. In some embodiments, the animation of the donor subjectmay be a realistic depiction of the donor subject. In other embodiments,the animation of the donor subject may be a cartoon of the donorsubject, or an altered version of the donor subject.

The method for generating and using a predictive virtual personificationmay also include rendering physio-emotional characteristics of thesubject at step 1235. For example, the physio-emotional characteristicsassociated with a donor subject may be stored in data store 630. Thesephysio-emotional characteristics are correlated SROI specific saliencymaps associated with various stimulus events. Data store 630 may alsostore a database organizing various stimulus events by label and type.In some embodiments, the saliency data collected from the donor subjectmay only relate to a subset of the available stimulus event types and/orlabels known to the predictive virtual personification system. However,PVP rendering engine 626 may extrapolate the correlated SROI specificsaliency maps generated from the AV data sets of the donor subject tocreate rendered SROI specific saliency maps that cover a complete set ofprobable stimulus events. For example, PVP rendering engine 626 maycompare correlated SROI specific saliency maps for one donor subject inreaction to a subset of stimulus events to other subjects reaction tothe same types of stimulus events to identify closely matching profiles,and differences within those profiles between the one donor subject andthe other subjects. The PVP rendering engine may then use thosedifferences to extrapolate to the one donor subject a complete set ofrendered SROI specific saliency maps for stimulus event data collectedfrom the other subjects. Accordingly, PVP rendering engine 626 mayeither recall, or calculate on-the-fly a set of rendered SROI specificsaliency maps approximating how a donor subject would react to any ofthe stimulus events and/or stimulus event types known to the predictivevirtual personification system.

In some embodiments, more than one set of correlated SROI specificsaliency maps will be captured and stored for a specific stimulus event.Thus, a donor subject may react differently, with some slight variancesto a specific stimulus event. The rendering of the physio-emotionalcharacteristics may also include applying a Bayesian probabilityfunction to determine which set of correlated SROI specific saliencymaps to apply in response to a specific stimulus event, in light of thehistoric response pattern a donor subject may have had to similarstimulus events.

Rendering the physio-emotional characteristics of the subject at 1235may also include adapting correlated and/or rendered SROI specificsaliency maps using a CPF function. For example, as a donor subject isrepeatedly exposed to the same stimulus event, the donor subject'snatural reaction to estimates of may change. For example, the speed ofany reaction to the stimulus event may increase such that a patternlinking any preprocessing and/or preplanning by the donor subject inrelation to the donor subject active response to the stimulus event maybecome more predictable, more repeatable, and faster, following a CPFfunction. Thus, the CPF function may be applied to the rendered orcorrelated SROI specific saliency maps, increasing the probability thatany given map may be applied in response to a specific stimulus event,in fact altering that map by shortening the time between stimulus eventand active response.

The method for generating and using a predictive virtual personificationmay also include rendering environment at step 1245. For example, theenvironment may be any setting applicable to the donor subject. Forexample, the environment could be a baseball field, a golf tee box, astage of the music concert, a couch in the living room, or any otherplace that a person might find himself or herself. Environment renderingengine 628 may use known CGI methods to render the environment based onhistorical AV data of similar environment stored in data store 630.Alternatively, in some embodiments, environment rendering engine 628writers environment based on real-time AV data sets being capturedthrough AV source 610 such that a real-time environment may be providedto the predictive virtual personification.

The method for generating and using a predictive virtual personificationmay also include applying the geospatial animation of the donor subjectwith the physio-emotional characteristics at step 1255. For example, thepredictive virtual personification may be rendered within the renderedenvironment and exposed to one or more stimulus events. As saliencyrecognition engine recognizes the stimulus events, or the stimulusevents are either pre-populated or populated in real time into the PVPserver through the user interface, the PVP rendering engine can predicthow the donor subject would have reacted to the stimulus event, and mayapply a set of rendered SROI specific saliency maps to animate thepredictive virtual personification within the rendered environment. Incases where the rendered environment is drawn in real time from a realenvironment, the predictive virtual personification may be projectedinto the real environment through AV output device 640. In cases wherethe rendered environment is generated from historical or animated datasets, both the rendered environment and predictive virtualpersonification may be output through AV output device 640.

In some embodiments, social media users may share data, includingpersonalized CPF data and/or predictive virtual personification data,using one or more social media applications. For example, a social mediauser may use a the cognitive and geospatial signal analysis devicesdisclosed herein to capture a CPF for a particular task and upload thatCPF to the user's profile page within a social media application, andmay modify the CPF if changes occur over time. Similarly, other usersmay capture their own CPF data for performing the same task. A group maybe formed within the social media application enabling users to sharetheir CPFs, share CPF data, and use other user's CPF data as a benchmarkto enhance a training regimen. In one example, users may compete tolearn how to play golf, using a normalized CPF as an indicator of howwell each user has learned. One of skill in the art would appreciatethat social media users may compete in a wide variety of learningregimens covering any type of activity.

In some embodiments, social media users may capture AV data using amobile capture device, including a mobile phone camera and audiorecorder. The data may be uploaded through a network, such as theInternet, to a PVP server for analysis. The user may provide additionalinput to label SROI's and event maps, and selecting particular stimulusevents. The PVP server may then use the user input and AV data tocompile correlated SROI specific saliency maps, consistent withembodiments disclosed herein. The PVP server may also use the AV dataand correlated SROI specific saliency maps to render a predictivevirtual personification of the user, and the predictive virtualpersonification may be stored within a user's profile page within asocial media application. In some examples, the predictive virtualpersonification may interact with other users or other predictivevirtual personifications within one or more social media site by sendingand responding to messages, posts, and blogs. Embodiments disclosedherein enable users to share CPF and predictive virtual personificationdata to social media applications, such as FACEBOOK, INSTAGRAM, TWITTER,YOUTUBE, LINKED IN, and social media sites configured to enable themanagement of AV content. Users may configure their CPF and/orpredictive virtual personification data to automatically display throughsocial media applications, or any other web interface, for example,using HTTP, HTTPS, or other communications protocols, or using availableApplication Program Interfaces (API). Users may search, subscribe, view,and manage their data channels, and modify their associated metadatacontent from anywhere for rebroadcasting to anywhere.

In some embodiments, a user may store a predictive virtualpersonification on a social media site and share access to thepredictive virtual personification with a selected group of other users.Members of the group may have access not only to interact with thepredictive virtual personification via the social media application, butalso to talk to or virtually interact with the rendered predictivevirtual personification through video chat tools, for example, toolsembedded within a mobile wireless device such as FACETIME, SKYPE, orother similar tools. In one example, a receiving side device may includea video projector, a holographic projector, or an AV reality device suchas a virtual reality headset, to display the predictive virtualpersonification and allow members of the group to interact with thepredictive virtual personification.

In some embodiments, CPF and/or other predictive virtual personificationdata from a group or network of users may be compiled and correlatedwith respect to user demographic traits (e.g., gender, race, age,geography, religion, etc.) as related to personality, behavior, andlearning traits. The data may be captured from social media feeds,consistent with embodiments disclosed herein, manually entered, orcollected directly from one or more PVP servers. A central database maystore these correlated data sets for analysis by sociologists,psychologists, or other users interested in understanding demographicand environmental effects on human behavior.

As used herein, the term module might describe a given unit offunctionality that can be performed in accordance with one or moreembodiments of the technology disclosed herein. As used herein, a modulemight be implemented utilizing any form of hardware, software, or acombination thereof. For example, one or more processors, controllers,ASICs, PLAs, PALs, CPLDs, FPGAs, logical components, software routinesor other mechanisms might be implemented to make up a module. Inimplementation, the various modules described herein might beimplemented as discrete modules or the functions and features describedcan be shared in part or in total among one or more modules. In otherwords, as would be apparent to one of ordinary skill in the art afterreading this description, the various features and functionalitydescribed herein may be implemented in any given application and can beimplemented in one or more separate or shared modules in variouscombinations and permutations. Even though various features or elementsof functionality may be individually described or claimed as separatemodules, one of ordinary skill in the art will understand that thesefeatures and functionality can be shared among one or more commonsoftware and hardware elements, and such description shall not requireor imply that separate hardware or software components are used toimplement such features or functionality.

Where components or modules of the technology are implemented in wholeor in part using software, in one embodiment, these software elementscan be implemented to operate with a computing or processing modulecapable of carrying out the functionality described with respectthereto. One such example computing module is shown in FIG. 13. Variousembodiments are described in terms of this example-computing module1300. After reading this description, it will become apparent to aperson skilled in the relevant art how to implement the technology usingother computing modules or architectures.

Referring now to FIG. 13, computing module 1300 may represent, forexample, computing or processing capabilities found within desktop,laptop and notebook computers; hand-held computing devices (PDA's, smartphones, cell phones, palmtops, etc.); mainframes, supercomputers,workstations or servers; or any other type of special-purpose orgeneral-purpose computing devices as may be desirable or appropriate fora given application or environment. Computing module 1300 might alsorepresent computing capabilities embedded within or otherwise availableto a given device. For example, a computing module might be found inother electronic devices such as, for example, digital cameras,navigation systems, cellular telephones, portable computing devices,modems, routers, WAPs, terminals and other electronic devices that mightinclude some form of processing capability.

Computing module 1300 might include, for example, one or moreprocessors, controllers, control modules, or other processing devices,such as a processor 1304. Processor 1304 might be implemented using ageneral-purpose or special-purpose processing engine such as, forexample, a microprocessor, controller, or other control logic. In theillustrated example, processor 1304 is connected to a bus 1302, althoughany communication medium can be used to facilitate interaction withother components of computing module 1300 or to communicate externally.

Computing module 1300 might also include one or more memory modules,simply referred to herein as main memory 1308. For example, preferablyrandom access memory (RAM) or other dynamic memory, might be used forstoring information and instructions to be executed by processor 1304.Main memory 1308 might also be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 1304. Computing module 1300 might likewise includea read only memory (“ROM”) or other static storage device coupled to bus1302 for storing static information and instructions for processor 1304.

The computing module 1300 might also include one or more various formsof information storage mechanism 1310, which might include, for example,a media drive 1312 and a storage unit interface 1320. The media drive1312 might include a drive or other mechanism to support fixed orremovable storage media 1314. For example, a hard disk drive, a floppydisk drive, a magnetic tape drive, an optical disk drive, a CD or DVDdrive (R or RW), or other removable or fixed media drive might beprovided. Accordingly, storage media 1314 might include, for example, ahard disk, a floppy disk, magnetic tape, cartridge, optical disk, a CDor DVD, or other fixed or removable medium that is read by, written toor accessed by media drive 1312. As these examples illustrate, thestorage media 1314 can include a computer usable storage medium havingstored therein computer software or data.

In alternative embodiments, information storage mechanism 1310 mightinclude other similar instrumentalities for allowing computer programsor other instructions or data to be loaded into computing module 1300.Such instrumentalities might include, for example, a fixed or removablestorage unit 1322 and an interface 1320. Examples of such storage units1322 and interfaces 1320 can include a program cartridge and cartridgeinterface, a removable memory (for example, a flash memory or otherremovable memory module) and memory slot, a PCMCIA slot and card, andother fixed or removable storage units 1322 and interfaces 1320 thatallow software and data to be transferred from the storage unit 1322 tocomputing module 1300.

Computing module 1300 might also include a communications interface1324. Communications interface 1324 might be used to allow software anddata to be transferred between computing module 1300 and externaldevices. Examples of communications interface 1324 might include a modemor softmodem, a network interface (such as an Ethernet, networkinterface card, WiMedia, IEEE 802.XX or other interface), acommunications port (such as for example, a USB port, IR port, RS232port Bluetooth® interface, or other port), or other communicationsinterface. Software and data transferred via communications interface1324 might typically be carried on signals, which can be electronic,electromagnetic (which includes optical) or other signals capable ofbeing exchanged by a given communications interface 1324. These signalsmight be provided to communications interface 1324 via a channel 1328.This channel 1328 might carry signals and might be implemented using awired or wireless communication medium. Some examples of a channel mightinclude a phone line, a cellular link, an RF link, an optical link, anetwork interface, a local or wide area network, and other wired orwireless communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as, forexample, memory 1308, storage unit 1320, media 1314, and channel 1328.These and other various forms of computer program media or computerusable media may be involved in carrying one or more sequences of one ormore instructions to a processing device for execution. Suchinstructions embodied on the medium, are generally referred to as“computer program code” or a “computer program product” (which may begrouped in the form of computer programs or other groupings). Whenexecuted, such instructions might enable the computing module 1300 toperform features or functions of the disclosed technology as discussedherein.

While various embodiments of the disclosed technology have beendescribed above, it should be understood that they have been presentedby way of example only, and not of limitation. Likewise, the variousdiagrams may depict an example architectural or other configuration forthe disclosed technology, which is done to aid in understanding thefeatures and functionality that can be included in the disclosedtechnology. The disclosed technology is not restricted to theillustrated example architectures or configurations, but the desiredfeatures can be implemented using a variety of alternative architecturesand configurations. Indeed, it will be apparent to one of skill in theart how alternative functional, logical or physical partitioning andconfigurations can be implemented to implement the desired features ofthe technology disclosed herein. Also, a multitude of differentconstituent module names other than those depicted herein can be appliedto the various partitions. Additionally, with regard to flow diagrams,operational descriptions and method claims, the order in which the stepsare presented herein shall not mandate that various embodiments beimplemented to perform the recited functionality in the same orderunless the context dictates otherwise.

Although the disclosed technology is described above in terms of variousexemplary embodiments and implementations, it should be understood thatthe various features, aspects and functionality described in one or moreof the individual embodiments are not limited in their applicability tothe particular embodiment with which they are described, but instead canbe applied, alone or in various combinations, to one or more of theother embodiments of the disclosed technology, whether or not suchembodiments are described and whether or not such features are presentedas being a part of a described embodiment. Thus, the breadth and scopeof the technology disclosed herein should not be limited by any of theabove-described exemplary embodiments.

Terms and phrases used in this document, and variations thereof, unlessotherwise expressly stated, should be construed as open ended as opposedto limiting. As examples of the foregoing: the term “including” shouldbe read as meaning “including, without limitation” or the like; the term“example” is used to provide exemplary instances of the item indiscussion, not an exhaustive or limiting list thereof; the terms “a” or“an” should be read as meaning “at least one,” “one or more” or thelike; and adjectives such as “conventional,” “traditional,” “normal,”“standard,” “known” and terms of similar meaning should not be construedas limiting the item described to a given time period or to an itemavailable as of a given time, but instead should be read to encompassconventional, traditional, normal, or standard technologies that may beavailable or known now or at any time in the future. Likewise, wherethis document refers to technologies that would be apparent or known toone of ordinary skill in the art, such technologies encompass thoseapparent or known to the skilled artisan now or at any time in thefuture.

The presence of broadening words and phrases such as “one or more,” “atleast,” “but not limited to” or other like phrases in some instancesshall not be read to mean that the narrower case is intended or requiredin instances where such broadening phrases may be absent. The use of theterm “module” does not imply that the components or functionalitydescribed or claimed as part of the module are all configured in acommon package. Indeed, any or all of the various components of amodule, whether control logic or other components, can be combined in asingle package or separately maintained and can further be distributedin multiple groupings or packages or across multiple locations.

Additionally, the various embodiments set forth herein are described interms of exemplary block diagrams, flow charts and other illustrations.As will become apparent to one of ordinary skill in the art afterreading this document, the illustrated embodiments and their variousalternatives can be implemented without confinement to the illustratedexamples. For example, block diagrams and their accompanying descriptionshould not be construed as mandating a particular architecture orconfiguration.

1. A method for generating a predictive virtual personificationcomprises: receiving a data set comprising a graphical representation ofa donor subject; receiving, from a wearable data capture device, aneuro-cognitive data set; locating within the data set, with a saliencyrecognition engine, a set of saliency regions of interest (SROI) withinsaid graphical representation of the donor subject; identifying one ormore trigger stimulus events, wherein each trigger stimulus eventprecedes or is contemporaneous with one or more SROI specific reactiveresponses and each SROI specific reactive response is observable withina SROI; temporally correlating the SROI specific reactive response witha subset of the neuro-cognitive data set; analyzing the subset of theneuro-cognitive or physio-emotional data set to identify a set ofdonor-specific physio-emotional or neuro-cognitive characteristicscorresponding to a donor-specific physio-emotional or neuro-cognitivestate at the time of the trigger stimulus event; generating, for eachSROI, a set of SROI specific saliency maps, wherein each SROI specificsaliency map plots a change in of one or more SROIs within apredetermined time-frame corresponding to each trigger stimulus event;and storing, in a data store, a set of correlated SROI specific saliencymaps generated by correlating each SROI specific saliency map to acorresponding trigger event and the donor-specific physio-emotional orneuro-cognitive state.
 2. The method of claim 1, further comprisingconfirming the identification of the identified set of donor-specificphysio-emotional or neuro-cognitive characteristics by receiving one ormore physio-emotional characteristic tags corresponding to a knownphysio-emotional state of the donor subject.
 3. The method of claim 2,wherein the receiving of one or more physio-emotional characteristictags comprises receiving input from a user interface.
 4. The method ofclaim 2, wherein the identifying a set of donor-specificphysio-emotional or neuro-cognitive characteristics comprises using adeep learning algorithm to match one or more correlated SROI specificsaliency maps with a plurality of historical SROI specific saliencymaps, wherein each historical SROI specific saliency map corresponds toa set of physio-emotional characteristics.
 5. The method of claim 1,further comprising confirming the identification of a set ofdonor-specific neuro-cognitive characteristics from the neuro-cognitivedata set by receiving one or more neuro-cognitive characteristic tagscorresponding to a known neuro-cognitive state of the donor subject. 6.The method of claim 5, wherein the receiving of one or moreneuro-cognitive characteristic tags comprises receiving input from auser interface.
 7. The method of claim 6, wherein the identifying a setof donor-specific physio-emotional or neuro-cognitive characteristicscomprises using a deep learning algorithm to match one or morecorrelated SROI specific saliency maps with a plurality of historicalSROI specific saliency maps, wherein each historical SROI specificsaliency map corresponds to a set of neuro-cognitive characteristics. 8.The method of claim 3, wherein the matching the one or more correlatedSROI specific saliency maps with a plurality of historical SROI specificsaliency maps comprises applying, with the PVP correlation engine, arenormalization group transformation to each historical specificsaliency map to generate a predictive saliency map space.
 9. The methodof claim 7, wherein the matching the one or more correlated SROIspecific saliency maps with a plurality of historical SROI specificsaliency maps comprises applying, with the PVP correlation engine, arenormalization group transformation to each historical specificsaliency map to generate a predictive saliency map space.
 10. The methodof claim 1, wherein the set of donor-specific physio-emotional orneuro-cognitive characteristics comprises mood, level of rest, level ofstress, or health.
 11. The method of claim 1, further comprisinggenerating, with a graphical rendering engine, an animatedrepresentation of the donor subject using the data set.
 12. The methodof claim 11, further comprising exposing the animated representation ofthe donor subject to a secondary stimulus event and rendering for eachSROI, with a PVP rendering engine, a predicted reactive response. 13.The method of claim 7, wherein the rendering a predicted reactiveresponse comprises identifying a secondary set of physio-emotional orneuro-cognitive characteristics corresponding to the animatedrepresentation of the donor subject; identifying one or more triggerstimulus events corresponding to the secondary stimulus event;receiving, from the data store, each set of correlated SROI specificsaliency maps corresponding to each identified trigger stimulus eventand to the identified set of physio-emotional or neuro-cognitivecharacteristics; and generating, with the PVP rendering engine, a set ofpredictive SROI-specific saliency maps based on a probabilisticextrapolation as a function of the correlated SROI specific saliencymaps, the identified physio-emotional or neuro-cognitivecharacteristics, and the identified trigger stimulus event.
 14. Themethod of claim 13, wherein the generating a set of predictiveSROI-specific saliency maps comprises collecting the correlated SROIspecific saliency maps into a historical saliency map space and applyinga deep learning algorithm to the historical saliency map space togenerate a predictive saliency map space.
 15. The method of claim 13,wherein the generating a set of predictive SROI-specific saliency mapscomprises collecting the correlated SROI specific saliency maps into ahistorical saliency map space and applying a renormalization grouptransformation to the historical saliency map space to generate apredictive saliency map space.
 16. The method of claim 14, furthercomprising rendering, with the graphical rendering engine, a geospatialmovement of the animated representation of the donor subject by applyingthe set of predictive SROI-specific saliency maps to each SROI withinthe animated representation of the donor subject.
 17. A system forgenerating a predictive virtual personification comprises: a wearabledata acquisition device, a data store, and a saliency recognitionengine; wherein the wearable data acquisition device is configured totransmit a physio-emotional or neuro-cognitive set to the saliencyrecognition engine; and the saliency recognition engine comprises anon-transitory computer readable medium with a set of computerexecutable instructions stored thereon, the computer executableinstructions configured to receive the physio-emotional orneuro-cognitive data set, a geospatial data set comprising a graphicalrepresentation of a donor subject, and one or more identified triggerstimulus events, wherein each identified trigger stimulus eventsprecedes or is contemporaneous with one or more saliency regions ofinterest (SROI) specific reactive responses and each SROI specificreactive response is observable within an SROI; locate a set of SROIwithin the graphical representation of the donor subject; generate, foreach SROI, a set of SROI specific saliency maps, wherein each SROIspecific saliency map plots a change in geospatial orientation of one ormore SROIs within a predetermined time-frame corresponding to eachtrigger stimulus event; temporally correlate the SROI specific reactiveresponse with a subset of the physio-emotional or neuro-cognitive dataset; analyze the subset of the physio-emotional or neuro-cognitive dataset to identify a set of donor-specific physio-emotional orneuro-cognitive characteristics corresponding to a donor-specificphysio-emotional state at the time of the trigger stimulus event; andstore, in the data store, a set of correlated SROI specific saliencymaps generated by correlating each SROI specific saliency map acorresponding trigger event and the donor-specific physio-emotional orneuro-cognitive state.
 18. The system of claim 17, wherein the wearabledata acquisition device comprises an MEG, an EEG, a video camera, or amotion capture device (MOCAP).
 19. The system of claim 17, wherein thesaliency recognition engine is further configured to identify a set ofdonor-specific physio-emotional or neuro-cognitive characteristicscorresponding to the donor-specific physio-emotional state and tag theset of correlated SROI specific saliency maps with the corresponding setof donor-specific physio-emotional or neuro-cognitive characteristics.20. The system of claim 17, wherein the saliency recognition engine isfurther configured to apply a deep-learning algorithm to identify a setof donor-specific physio-emotional or neuro-cognitive characteristicscorresponding to the donor-specific physio-emotional state and tag theset of correlated SROI specific saliency maps with the corresponding setof donor-specific physio-emotional or neuro-cognitive characteristics.21. The system of claim 17, further comprising a graphical renderingengine configured to generate an animated representation of the donorsubject based on the physio-emotional or neuro-cognitive data.
 22. Thesystem of claim 21, further comprising a predictive virtual presentation(PVP) rendering engine configured to generate a predicted reactiveresponse to a secondary stimulus event by applying a deep learningalgorithm.
 23. The system of claim 22, wherein the PVP rendering engineis further configured to: identify a secondary set of physio-emotionalor neuro-cognitive characteristics corresponding to the animatedrepresentation of the donor subject; identify one or more triggerstimulus events corresponding to the secondary stimulus event; receive,from the data store, each set of correlated SROI specific saliency mapsthat correspond to each identified trigger stimulus event and to theidentified set of physio-emotional or neuro-cognitive characteristics;and generate a set of predictive SROI-specific saliency maps based on aprobabilistic extrapolation as a function of the correlated SROIspecific saliency maps, the identified physio-emotional orneuro-cognitive characteristics, and the identified trigger stimulusevent.
 24. The system of claim 23, wherein the graphical renderingengine is further configured to render geospatial movement of theanimated representation of the donor subject by applying the set ofpredictive SROI-specific saliency maps to each SROI within the animatedrepresentation of the donor subject.